A systematic review of clinical supervision evaluation studies in nursing
Bibliographic record
Abstract
According to the international, extant literature published during the last 20 years or so, clinical supervision (CS) in nursing is now a reasonably common phenomenon. Nevertheless, what appears to be noticeably 'thin on the ground' in this body of literature are empirical evaluations of CS, especially those pertaining to client outcomes. Accordingly, the authors undertook a systematic review of empirical evaluations of CS in nursing to determine the state of the science. Adopting the approach documented by Stroup et al. (JAMA, 283, 2000, 2008), the authors searched for reports of evaluation studies of CS in nursing - published during the years 1995 to 2015. Keywords for the search were 'clinical supervision', 'evaluation', 'efficacy', 'nursing', and combinations of these keywords. Electronic databases used were CINAHL, MEDLINE, PsychLIT, and the British Nursing Index. The research evidence from twenty-eight (28) studies reviewed is presented, outlining the main findings with an overview of each study presented. The following broad themes were identified and are each discussed in the study: narrative/anecdotal accounts of positive outcomes for clinical supervision, narrative/anecdotal accounts of negative outcomes for clinical supervision, empirical positive outcomes reported by supervisee, and empirical findings showing no effect by supervisee.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".