It's more than just a referral: Development of an evidence-informed exercise and depression toolkit
Bibliographic record
Abstract
The aim of this article is to describe this systematic and phased process in developing the evidence-based ‘Exercise and Depression Toolkit’ for health care providers working with adults with depression. The Appraisal of Guidelines, Research and Evaluation (AGREE) II tool was consulted throughout the developmental phased process, and used to guide toolkit content and dissemination strategies. The four phases included a review of relevant literature, formative interviews, an expert panel meeting, and finally toolkit development. A Theoretical Domains Framework (TDF) analysis was also used to determine behaviour change techniques (BCT) to be included in the toolkit. Various stakeholders were involved throughout the process including health care providers, adults who have lived experience with depression, researchers, and exercise professionals who have experience working with adults with depression. Recommendations from the consultation process included that the toolkit be ‘depression tailored’ including specific barriers that adults with depression face to engaging in physical activity (PA) and strategies they can use. The toolkit should promote collaboration and a person-centered approach. Different parts of the toolkit should be created for the intended audience of health care providers and adults with depression. BCTs were included to target the ‘Emotion’ and ‘Social Influences’ domains of the TDF. These recommendations have resulted in the development of the ‘Exercise and Depression Toolkit’. This toolkit is a resource for health care providers, adults with depression, and exercise professionals to help exercise become an accessible treatment option for the many Canadians living with depression.
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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 | 0.259 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".