Research utilization among children's mental health providers
Bibliographic record
Abstract
BACKGROUND: Children with emotional and behavioural disorders should be able to count on receiving care that meets their needs and is based on the best scientific evidence available, however, many do not receive these services. Implementation of evidence-based practice (EBP) relies, in part, on the research utilization practices of mental health care providers. This study reports on a survey of research utilization practices among 80 children's mental health (CMH) service provider organizations in Ontario, Canada. METHODS: A web-based survey was distributed to 80 CMH service provider organizations, to which 51 executive directors and 483 children's mental health practitioners responded. Research utilization was assessed using questions with Likert-type responses based on the Canadian Health Services Research Foundation's Four-A's approach: access, assess, adapt, apply. RESULTS: There was general agreement among executive directors and practitioners regarding the capacity of their organizations to use - access, assess, adapt, and apply - research evidence. Overall, both groups rated their organizations as using research information 'somewhat well.' The low response rate to the practitioner survey should be noted. CONCLUSION: These findings provide a useful benchmark from which changes in reported research utilization in the Ontario CMH sector can be tracked over time, as a function of EBP training and implementation initiatives, for instance. The need to improve access to research evidence should be addressed because it relates to the eventual implementation and uptake of evidence-based practices. Communities of practice are recommended as a strategy that would enable practitioners to build capacity in their adaptation and application of research evidence.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".