Evaluation of health care providers’ use of the ‘Exercise and Depression Toolkit’: a case study
Bibliographic record
Abstract
BACKGROUND: Exercise is now recommended as a primary treatment for mild-moderate depression in Canada. The 'Exercise and Depression Toolkit' was developed to help health care providers (HCP) integrate these treatment guidelines into practice. The purpose of this study was to evaluate acceptability and perceived effectiveness of the toolkit in practice by HCPs working with individuals with depression. METHODS: A case study design was utilized. The toolkit was given to 6 HCPs to use in practice for 4 weeks. Pre- and post-intervention phone interviews were conducted, and weekly logs were provided to track use and satisfaction of interactions with individuals with depression when using the toolkit. The study was conceptually guided by a hybrid theoretical approach using the Diffusion of Innovation Theory and the Theoretical Framework of Acceptability. RESULTS: All HCPs used the toolkit at least once. Participants viewed their interactions when using the toolkit to be successful (considering individuals' receptiveness, its usefulness and general satisfaction.) The average success score for all participants was 5.5/7. HCPs found the toolkit to be acceptable. All participants (n = 6) viewed the toolkit as having relative advantage in helping them to discuss exercise with individuals with depression, and as relatively simple and easy to use (not complex) and adaptable to their practice needs (having trialability). Participants liked the toolkit and had mostly positive things to say about it. Participants had mixed feelings about whether changes in the people they worked with (such as mood and activity levels) could be observed (observability) and whether the toolkit changed their belief in their ability to recommend or discuss exercise (self-efficacy). Recommended dissemination strategies were adopted in promoting the toolkit. CONCLUSIONS: Future work should address observability and the ability for health care providers to see other providers using it, as well as effectiveness considering outcomes for people with depression such as mood and activity changes. The results of this initial evaluation seem promising for uptake and future adoption of the toolkit by health care providers working with adults with depression in Canada.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".