Quantitative Data

The quantitative data sources were imputed into an EXCEL spread sheet by the researcher as a master data document. This included Day Logs, Consultation Logs, Study Patient Lists, and SCNSs. Data entry keys were used for Consultation Logs and SCNSs, but were unnecessary for the Study Patient Lists and Day Logs as the data imputation was straightforward. Separate files were created within Statistical Package for the Social Sciences (SPSS Packages 8 through to 16) to analyse each data collection tool.

Quantitative data from Day Logs, Consultation Logs, and SCNSs were analysed by using simple descriptive statistics within SPSS. The researcher utilised the Study Patient Lists to determine the number of women seen by each RBN, the number and timing of RBN consultations per patient. Day Logs were utilised to describe the amount and proportion of time spent on various RBN duties, while the Consultation Logs were used to analyse the dose, delivery, and content of consultations, typically in the form of means and percentages. For example, dose data were analysed to extract the percentage of clients receiving RBN consultations across the treatment phases, and the mean number of consultations received by clients.

The SCNS was analysed for the prevalence of perceived unmet needs, which is the frequency with which women reported each item to be an area of moderate or high unmet need. Additionally, the prevalence of moderate or high unmet need for access to services were analysed in the same way. The mean number of unmet

112 needs and the mean number of unmet needs within domains were identified. The relative magnitude of unmet need was compared with other relevant samples whenever possible. Descriptive statistics were sought for the medical and demographic characteristics of the samples. Appropriate tests of statistical

significance were sought when applicable. T-tests for paired samples were used for parametric data (i.e. two samples per subject and normally distributed) and non- parametric data were handled with Wilcoxon Signed Ranks Tests for related samples (i.e. two samples per subject while not assuming normally distributed) or Chi squared tests (i.e. two samples per subject while not assuming normally distributed).

Generally speaking, response rates for RBNs and women with breast cancer were well within acceptable levels (Australian Government Statistical Clearing House, 2004). RBN participants demonstrated good adherence to the research protocol and response rates. One hundred percent of Day Logs and Study Patient Lists were returned. However, the completion and return rate of Consultation Logs was unable to be determined as the true number of consultations is unknown. Yet, as 1039 consultations were recorded on Study Patient Lists and 904 Consultations Logs were returned, an approximate 87% response rate for Consultations Logs was received. The response rates of women with breast cancer were also high, with SCNS response rates around 80%.

The researcher was unable to draw direct comparisons of the effectiveness of different RBN sites as the settings and their clients were inherently different16. Nonetheless, the thesis provides a comprehensive description of rural breast nursing practices, the influences on these practices, and the extent to which their clients’ supportive care needs were met.

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4.6.1.1. Quantitative Analysis Issues

Two specific analysis issues for consideration in this study are missing data and overlapping data. These have the potential to impact the analysis, results, and decisions about results for presentation. Below is a description of the analysis results which guided decision-making about how to handle missing and overlapping data.

4.6.1.1.1. Missing Data

How the missing data was handled has an impact on the analysis undertaken and results received. The researcher was guided by the principles of understanding the data, and ensuring the results were the truest reflection possible of the real world circumstance. "Excluding all cases with missing data can bias the

representativeness of the findings, but including such cases by assigning scores to missing data can influence the nature of the findings. The safest and best method is to construct the index using more than one of these methods and see whether you reach the same conclusions using each of the indexes. Understanding your data is the final goal of analysis anyway" (Babbie, 2007, p.164-165).

There was a relatively small amount of missing item data. The missing data is unlikely to challenge the validity of the quantitative analysis results as the response rates were high and the missing data was not found to demonstrate bias. This section describes how missing data was handled. Missing data was considered for Consultations Logs and SCNSs, as these were the only tools where it was possible to identify missing data.

Item non-response, leading to missing data points, affected the SCNS and

Consultation Log analysis. SCNSs with missing data were excluded when analysing the prevalence of unmet needs for specific items and overall. Similarly, when data points were missing within Consultations Logs, those Logs were excluded from analysis of individual elements of breast nursing practices. For example, of the 904 Consultation Logs only 853 Logs contained consultation duration data (94% item

114 response rate, or 6% missing data). Thus the mean consultation duration was analysed using only the available data points (n=853). This was also true for consultation treatment phase (7% missing data, 60 of 904) and face-to-face or telephone (12.5% of all data points were unavailable, 113 of 904). Results with excluded missing data points were compared to those including missing data points, and in general it appeared that the missing data were consistently spread across categories. That is, the analysis results did not vary greatly. Thus, it did not appear that excluding the cases with missing data points would result in an

unrepresentative sample, nor make the number insufficient for analysis. However, a large percentage of missing initiation source data was found in the Consultation Logs (i.e. 41% of all data points were unavailable, 374 of 904). For this reason, initiation source results were presented in terms of the actual percentages that were known. The researcher searched for patterns in initiation source non- response17. Reasons for non-response on this item may be due to uncertainty about initiation definitions, perceptions of non-importance or irrelevance, and/or time pressures. Two RBNs in particular had very high non-response rates, 67% and 68%. It is unlikely that the question was challenging for RBN participants, and instead was simply skipped. It may be that some RBNs assumed all consultations would be RBN initiated unless otherwise stated.

4.6.1.1.2. Overlapping Data

How overlapping data points were handled could also impact the study results. This was an issue for data indicating the treatment phases during which consultations occurred since it was collected in both Consultation Logs and Study Patient Lists.

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The pattern of initiation source non-response was as follows: decreased over consultation phases, greatest for the Community RBN Site (50%) and smallest for Public/Private Hospital RBN Site (35%). One Community RBN demonstrated the highest non-response with one Private Hospital RBN close behind (68% and 67%

respectively). The variability in item non-response for initiation source across sites suggests that there may have been many different reasons for non-response.

115 Thus the analysis of consultation timing was able to be compared across two tools. For instance, Study Patient Lists reported 57% of consultations within specified treatment phases, whereas Consultation Logs described 70% during the same time. The Study Patient Lists appear to be a more reliable source of consultation timing data as there were 904 Consultation Logs submitted and 1039 consultations recorded within the Study Patient Lists. It was therefore assumed not all consultations were recorded using Consultation Logs, resulting in a discrepancy between the total consultations recorded in the two data sources. The missing Consultation Logs amounted to 13% of the total consultations recorded in Study Patient Lists (i.e. 135 consultations). This obviously had implications on analysis and results using Consultation Logs, which was discussed in the Missing Data section above.

The researcher searched for patterns in non-completion. Direct comparison of the two tools was possible for the number of ‘other’ consultations and their initiation source. There was a relatively even distribution of missing data between RBN and client-initiated consultations, especially when the missing data was assumed to be RBN-initiated. The total number of ‘other’ consultations reported via Consultation Logs accounted for only 57% of the total ‘other’ consultations reported within Study Patient Lists (n=252 and n= 446 respectively). It therefore seems likely that ‘other’ consultations were those most often excluded from Consultation Log data.

Reasons for Consultation Log non-completion were considered. It is possible the missing Logs were due to procedural issues (e.g. RBNs had consultations and recorded them on the Study Patient List, but no Consultation Log was completed and/or submitted). For instance, an RBN and client may have had an unplanned consultation after they inadvertently met around a cancer service site.

Therefore, Study Patient List data and was used for ratios, percentages, and total numbers of consultations, while Consultation Log data was used for descriptions of breast nursing practices during consultations.

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