The relationship between the RESET team’s presence and reports of child sexual abuse was investigated. The data collected during Operation RESET included 135 incident reports of child sexual abuse, with 124 field investigations conducted, leading to 24 arrests. In order to explore the effect of the presence of the RESET team in the communities, the proximity of the team’s presence to the incidence reports was investigated. More than 72% of incidence reports were made during or within three working days of Operation RESET’s presence (proactive and reactive) in the communities, as shown in Figure 7.1.
Figure 7.1. Number of reports of child sexual abuse by number of working days since RESET team presence in communities.
0 10 20 30 40 50 60 70 During 1 2 3 4 5 6 7 8 9 10‐13. 14+ Number of reports of CSA Days post team presence
Further information was obtained by the Operation RESET team regarding the prosecutorial outcomes of those arrested and charged during the course of Operation RESET and revealed an overall conviction rate of 54% (13 out of the 24 cases led to convictions). Of the eleven cases that were not convicted, nine were dismissed by the Director of Public Prosecutions and two were acquitted. Further analysis revealed that for the first nine months of the operation, of the 12 cases that were charged, seven (58%) proceeded to court. Of those, five were convicted, giving a successful prosecution rate of 71%. For the second nine months of the operation, eight out of 12 cases proceeded to court (66.7%), and all of these cases were successfully prosecuted (100%). These results indicate that there was an improvement in conviction rates as the model used by the RESET team was implemented.
Section Two: Data collected through the WA Police Incident Management System
Data for reports, arrests and arrests rates for the six regions, with the Mid- West Gascoyne and Pilbara regions split by intervention/non-intervention, are
presented in Table 7.3. As can be seen by the population estimates for the Indigenous population, the areas of the Mid-West Gascoyne that were not covered by the
intervention were small in comparison to those covered by the intervention, making up less than 10% of the total Indigenous population for the region.
Table 7.3
Numbers of Reports, Arrests and Arrest Rates, Indigenous Population Estimates and Percentage of Indigenous Population per Total Population by Time-period
Time period Reports Arrests a % A of IR b Indigenous
Popn estimatesc Indigenous per total popn a. MWG intervention 5392 11.1 TP1-pre 49 18 36.7 TP2-during 74 41 55.4 TP3-post 63 38 60.3 Total 186 97 52.2 b. MWG non-intervention 584 5.4 TP1-pre 13 7 53.8 TP2-during 24 12 50.0 TP3-post 8 4 50.0 Total 45 23 51.1 c. Pilbara intervention 801 32.5 TP1-pre 14 6 42.8 TP2-during 18 8 44.4 TP3-post 12 6 58.3 Total 44 21 47.7 d. Pilbara non-intervention 6410 11.2 TP1-pre 53 26 49.1 TP2-during 45 20 44.4 TP3-post 27 8 29.6 Total e. Goldfieldsd 4723 11.3 TP1-pre 67 38 56.7 TP2-during 66 29 43.9 TP3-post 53 22 41.5 Total 186 89 47.8 f. Great Southern 1650 3.0 TP1-pre 68 42 61.8 TP2-during 69 42 60.9 TP3-post 54 26 48.1 Total 191 110 57.6 g. Wheatbelt 3747 4.9 TP1-pre 55 23 41.8 TP2-during 56 23 41.1 TP3-post 52 28 53.8 Total 163 74 45.4 h. South-west 3714 2.2 TP1-pre 76 29 38.2 TP2-during 86 36 41.9 TP3-post 83 38 45.8 Total 245 103 42.0
Note. TP1-pre = the time period prior to the intervention; TP2-during = the time period of the
intervention; TP3-post = the time period post the intervention. a Arrests includes ‘Offender Processed.’ b % A of IR= percentage of arrests from incident reports. c Australian Bureau of Statistics (2013a).
Numbers of reports increased between time periods one and two (pre and during intervention) for the Mid-West Gascoyne intervention and non-intervention areas (see Table 7.3a and b) which could be attributable to the presence of the Operation RESET team in this region. Given that the size of the Indigenous
population in the non-intervention Mid-West Gascoyne areas is only approximately 10% of the total region, and that there could be expected spill-over from the
intervention into these nearby communities, it is not surprising that there are also increases in reports in the non-intervention areas of the Mid-West Gascoyne.
Number of reports also increased in the Pilbara intervention region (see Table 7.3c), but not in the Pilbara non-intervention areas (see Table 7.3d). In order to test the significance of changes between time periods by intervention status, three GEE models were produced for reports, arrests and arrest rates. Means with standard errors for number of reports per time period by intervention status for the models are shown in Table 7.4.
Reports. The GEE analysis of the number of reports indicated that the
intervention by time interaction effect was not significant (Wald x2 (2, N = 24) =
4.61, p = 0.10). There were significant differences for both the main effects (Intervention: Wald x2 (1, N = 24) = 5.09, p = 0.02; Time: Wald x2 (2, N = 24) =
60.85, p < 0.001). Paired comparisons indicated that mean number of reports increased from TP1 to TP2 in the intervention areas (Mean difference = 9.67, SE = 2.60, p < 0.001), but not for the non-intervention areas (Mean difference = 2.33, SE = 2.66, p = 0.38). There was a non-significant decrease in the intervention areas (Mean difference = 5.67, SE = 2.59, p = 0.03) and a significant decrease in reports for the
Table 7.4
Means and Standard Errors of Generalized Estimating Equations Analyses by Intervention and Time-period for Reports, Arrests and Arrest rates
Reports
M (SE) Arrests M (SE) Arrest rates M (SE)
Non- Intv TP1-pre 55.33 (8.37) 27.50 (4.61) 50.23 (3.36) TP2-during 57.67 (7.99) 27.00 (4.08) 47.04 (2.78) TP3-post 46.17(9.61) 21.00 (4.78) 44.81 (3.17) Intv TP1-pre 21.00 (6.55) 8.00 (1.63) 41.74 (4.16) TP2-during 30.67 (8.76) 16.33 (4.58) 53.31 (4.56) TP3-post 25.00 (11.03) 14.67 (5.89) 65.29 (6.77)
non-intervention areas between TP2 and TP3 (Mean difference = 11.20, SE = 2.85, p < 0.001).
Arrests. The GEE analysis of numbers of arrests indicated that the
intervention by time interaction effect was not significant (Wald x2 (2, N = 24) =
5.41, p = 0.07). There were significant differences for both the main effects (Intervention: Wald x2 (1, N = 24) = 4.54, p = 0.03; Time: Wald x2 (2, N = 24) =
15.94, p < 0.001). Paired comparisons indicated that there was a significant increase in numbers of arrests from TP1 to TP2 in the intervention areas (Mean difference = 8.33, SE = 3.07, p =.007), but not for the non-intervention areas (Mean difference = 0.50, SE = 2.29, p = 0.83). There was no significant change in number of arrests for the intervention areas between TP2 and TP3 (Mean difference = 1.67, SE = 1.66, p = 0.31), and a non-significant decrease in arrests for the non-intervention areas
between TP2 and TP3 (Mean difference = 6.00, SE = 3.01, p = 0.05).
Arrest rates. The GEE analysis of arrest rates indicated that the intervention
by time interaction effect was significant (Wald x2 (2, N = 24) = 22.32, p < 0.001).
Paired comparisons indicate that differences between intervention and non-
intervention areas increased over time. There was a significant increase in the arrest rate from TP1 to TP2 (Mean difference = 11.56, SE = 4.30, p = 0.007) as well as between TP2 to TP3 in the intervention areas (Mean difference = 11.98, SE = 3.47, p < 0.001), with no corresponding increases in the non-intervention areas (TP1-TP2: Mean difference = 3.19, SE = 2.07, p = 0.12; TP2-TP3: Mean difference = 2.24, SE = 3.86, p = 0.56).
In summary, reports and arrests increased between TP1 and TP2 in the intervention areas, but not in the non-intervention areas. Arrest rates increased in the intervention areas from TP1 to TP2 as well as from TP2 to TP3. There was no improvement in arrest rates between either time-period for the non-intervention areas.
Discussion
This study contains both the robust study design and reliable quantitative data required to evaluate changes in reporting rates of child sexual abuse during an
intervention designed to improve outcomes for children, their families and communities. Qualitative information gained from the first evaluation of the intervention (Mace et al., 2015) indicated that there was a positive response from service professionals and community members in the communities where the
intervention was implemented. The current study provides quantitative evidence that the intervention achieved its principal aim of increasing reporting rates.
The number of reports significantly increased in the intervention areas during the intervention compared to prior to the intervention, but did not increase in the non- intervention areas. Arrests likewise rose significantly in intervention areas but not in areas without the intervention between these time periods. A decrease in reports was evident in all intervention and non-intervention areas between TP2 and TP3,
however the decreases seen in the intervention areas were not significant. One possible explanation of the overall decline in reports after the intervention is that professionals may have had a better understanding of mandatory reporting criteria over this time. On the first of January, 2009, mandatory reporting legislation came
into effect in WA, making it a legal requirement for all doctors, nurses, midwives, teachers and police officers to report all reasonable beliefs of child sexual assault. This caused a rise in reporting over all regions of WA as the legislation became known (Western Australian Department for Child Protection, 2008). During the implementation of these changes, misunderstandings regarding the types of behavior that should be reported resulted in many reports being lodged that were outside the bounds of the legislation. During 2010, an education program was promoted across WA to improve understanding of child sexual abuse and behavior that required mandatory reporting. This in turn may have resulted in a decline in reports
throughout the year. No other major changes that could have affected reporting were observed, anecdotally or by police, during this time.
The decline in reporting in the intervention areas between TP2 and TP3 could be due to the better understanding of mandatory reporting as outlined above.
Alternatively, it could be due to offenders having been removed from intervention areas by police, which then reduced the number of incidences occurring. A further possible explanation could be that after withdrawal of the intervention team, Indigenous persons were less inclined to report due to the support from and
relationships with the RESET team no longer being available. The large arrest rate increase from TP2 to TP3 in the intervention areas, however, appears to indicate that the processes put in place by the team for the period post the intervention were successful. Arrests rates increased between TP1 and TP2 as well as between TP2 and TP3 in the intervention areas, whilst arrest rates remained stable in the non-
Analysis of the data collected on the team’s activities during Operation RESET indicated that there was a strong association between the group’s activities in the intervention areas and subsequent reporting behavior. Victims were most likely to report when the team was active in the intervention area. Importantly, successful conviction rates rose over the course of the operation’s presence in these
communities, suggesting that the processes put in place to support victims going through the court system were increasingly successful as the team and support services built rapport with community members over time.
One of the limitations of this study is the inherent difficulty in working with secondary data sources such as police databases, where data are collected primarily for operational rather than research purposes. In order to ensure the highest quality data, a data reconciliation process between the incident reports supplied by the Operation RESET team and comparative data from the Incident Management System for the RESET intervention areas was carried out. This revealed some discrepancies between the data sets, with a number of cases that appeared in the RESET team dataset not included in the Incident Management System data. Further analysis revealed that many of these cases in the Incident Management System had been entered as ‘child abuse’ but not ‘child sexual abuse’ and were therefore not within the search parameters. These cases were predominantly where no offender was found and the case did not proceed. In cases where this practice had happened and the case had progressed, the anomaly would have been amended as further data were added. It is reasonable to assume that this practice would be systematically equivalent across the entire Incident Management System dataset, and therefore we can consider that report and arrest outcomes across regions within this dataset would be comparable.
Prosecution and conviction rates between intervention and non-intervention areas were not contained in the Incident Management System, and it was not possible to obtain these at the time of publication. A comparative assessment of successful prosecutions between intervention and non-intervention sites using data for all regions in WA would be an important next step for further research.
Overall, improvements in the number of reports and arrests as well as arrest rates in the intervention areas are consistent with broader reform in the area of child sexual abuse in Indigenous communities, and support a rollout of the model used by Operation RESET. These findings, in conjunction with the prior qualitative study (Mace et al., 2015), suggest that these reforms have contributed to a major cultural shift and level of engagement between victims and service providers. The central concept underlying the development of the reforms is a more accessible, efficient, coordinated and user-friendly model of service delivery, where local solutions are developed for local problems. The findings of the current evaluation are entirely consistent with this concept.
As with any new system, there are opportunities for improvement. Further investment is required to isolate which elements of the intervention are the most important to implement in order to make the most efficient use of time and resources. Data need to be collated from numerous sources for ongoing evaluation, and
extensive record keeping is required. As more information becomes available through the rollout of future intervention programs, researchers can begin to determine the most significant components of the intervention and the most cost- effective means of implementing change.
The evidence from this evaluation, based on police data with a large and representative sample of communities, suggests that the reforms have corresponded with a marked improvement in some key outcomes for Indigenous victims of child sexual abuse. We recommend that all Australian jurisdictions consider moving to the more proactive model that has been employed in the Operation RESET intervention, and that these findings may be applicable to other Indigenous communities
CHAPTER EIGHT: QUALITATIVE EVALUATION OF OPERATION