‘Barriers to overcoming the barriers': A scoping review exploring 30 years of clinical supervision literature
Bibliographic record
Abstract
AIMS/QUESTIONS: To explore the barriers and facilitators to nurses accessing clinical supervision; explore the barriers and facilitators to organizations implementing clinical supervision and capture what skills nurses require to facilitate clinical supervision. DESIGN: Scoping review of peer-reviewed research and grey literature. DATA SOURCES: CINAHL, Medline, PsychINFO and Scopus were searched for relevant papers published between 1990 and 2020. Google, Google Scholar, OpenGrey & EThOS were used to search for grey literature. REVIEW METHODS: PRISMA-ScR guidelines were used during the literature review process. Eighty-seven papers were included, and data were extracted from each paper using a standardized form. Data synthesis was undertaken using Seidel's analytical framework. RESULTS: Five themes were identified: Definitions and Models, (Mis) Trust and the Language of Supervision, Alternative Parallel Forums and Support Mechanisms, Time and Cost and Skills required. CONCLUSION: Since its inception in the 1990s, clinical supervision has long been regarded as a supportive platform for nurses to reflect on and develop their practice. However, this review highlights that despite an awareness of the skills required for nurses to undertake clinical supervision, and the facilitators for nurses to access and organizations to implement clinical supervision, there have been persistent barriers to implementation. This review identifies these persistent factors as 'barriers to overcoming the barriers' in the clinical supervision landscape. These require critical consideration to contribute towards moving clinical supervision forward in the spirit of its original intentions. IMPACT: This review progresses the debate on clinical supervision through critically analysing the barriers to overcoming the barriers. To this end, the review is designed to stimulate critical discussions amongst nurses in different clinical spaces and key stakeholders such as policy makers and regulatory bodies for the nursing profession.
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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 | 0.052 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".