Clinical Supervision: Predicting Best Outcomes

Clinical Supervision [CS] has an increasingly established role in the working practices of human service agencies and, in particular, has been widely introduced into public and private health systems across the world. It has shown increasing international promise as a positive contribution to health governance agendas. The continuous measurement of CS efficacy, therefore, has become one of the most important contemporary challenges. The Manchester Clinical Supervision Scale^© [MCSS^©] has been adopted as the leading outcome measure of the effectiveness of CS in ~115 licenced Clinical Supervision evaluation studies, in 13 countries, and has been translated into seven languages other than English.

This presentation will report the latest progress of an ongoing program of Clinical Supervision research. It will draw on three recently completed interrelated research studies conducted by the authors, towards establishing an evidence base for policy and best practice development and robust evaluation of Clinical Supervision outcomes.

The first study was a large pragmatic randomised controlled trial of Clinical Supervision. It represented a rare attempt to establish an elusive set of demonstrable causal relationships between Clinical Supervision, the well-being of nurses, the quality of care they provided and the effect on patient-reported outcomes.

The second study will describe the process by which the original 36-item version of the Manchester Clinical Supervision Scale^© re-tested the original factor structure and response format for goodness of fit to the Rasch Model, using the latest RUMM 2030 software, Findings re-confirmed the validity of the response format of the 36-item version. They also indicated that original version could be reduced to 26 items, with increased structural integrity and result in improved fit statistics for six subscales, rather than the original seven. Thus, the Manchester Clinical Supervision Scale^© was transformed it into the present version; the MCSS-26^©.

The third study used real MCSS^© evaluation data and applied Classification and Regression Tree [CART] analyses; a relatively new statistical procedure which, to date, remains somewhat unknown. In operational terms, the output can be followed with little or no understanding of statistics and, to some extent, follows the decision process that nurses use to make decisions. For example, triage rules are now widely used in clinical settings to classify patients into various risk categories, so that appropriate decisions can be made regarding treatment.

Given the infinite range of international practice environments, health service organisations now have the opportunity to harvest measurement data using the Manchester Clinical Supervision Scale^© (MCSS-26^©) and to conduct CART analyses, that take account of particular local circumstances to model a range of delivery permutations which will predict the likelihood of the most effective arrangement for the delivery of Clinical Supervision.

The presentation will link these three completed empirical studies within the context of the so-called Proctor framework of Clinical Supervision. It will identify fresh theoretical insights, directions for policy development and professional practice, and future research possibilities.