Measurement-based care educational programmes for clinical trainees in mental healthcare: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Measurement-based care (MBC) represents the approach of regularly using symptom rating scales to guide patient care decisions in mental healthcare. MBC is an effective, feasible and acceptable approach to enhance clinical outcomes in various disciplines, including medicine, psychology, social work and psychotherapy. Yet, it is infrequently used by clinicians, potentially due to limited education for care providers. The objective of this scoping review is to survey the characteristics of MBC educational programmes for undergraduate, graduate and postgraduate clinical trainees in mental healthcare. METHODS AND ANALYSIS: Using database-tailored search strategies, we plan on searching Medline, PsycINFO, Embase, CINAHL and Cochrane Central for relevant studies. Thereafter, we will analyse the selected studies to extract information on the delivery of educational programmes, the clinical and educational outcomes of these programmes, and the potential enablers and barriers to MBC education. In this paper, we articulate the protocol for this scoping review. ETHICS AND DISSEMINATION: This scoping review does not require research ethics approval. The findings from this scoping review will be incorporated into the creation of a novel MBC curriculum and handbook. Results will be disseminated at appropriate national or international conferences, as well as in a peer-reviewed journal publication.
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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 | 0.103 | 0.073 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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".