Development of an Instrument to Measure the Level of Evidence-based Practice in Clinical Mental Health Services
Bibliographic record
Abstract
Introduction: While systems of care are identifying the need to employ evidence-based modes of treatment, there are to date few instruments designed to measure the degree to which clinicians apply evidence-based interventions. The interventions survey was designed to tap each clinicians’ understanding of evidence-based interventions and the degree to which they identify theory in relation to evidence-based intervention and diagnosis. Method: A literature review was conducted to identify evidence-based interventions. Search strategies included meta-analyses that focused on systematic review of a range of databases. Emphasis in the survey's design was placed on linking evidence-based interventions, diagnosis and theoretical orientations. Clinicians (n = 118) could select among a range of options including evidence-based interventions for specific diagnoses. In addition to mapping evidence to interventions by diagnosis, results include a report of index modal interventions used by staff by type (individual, group, family) and diagnosis. The higher the index reported (range 0-1) the greater the variation of interventions by diagnoses. Results: Forty percent of clinical staff favored more that one type of family therapy for different diagnoses, and 35% of staff favored more than one type of group therapy for different diagnoses, whereas about 30% of staff varied their choice of individual intervention as a function of diagnosis. Conclusions: The results indicate that the survey is able to tap a range of clinicians’ theoretical orientations and knowledge of evidence-based practices related to child and adolescent mental health interventions.
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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 | 0.123 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".