Bibliographic record
Abstract
For over 30 years, researchers have demonstrated that exercise effectively reduces depressive symptoms, yet only a limited number of Mental Health Professionals (MHP's) use exercise for its therapeutic effects. The first aim of the study is to identify the facilitators and barriers that MHP's encounter when considering exercise as an antidepressant treatment in clinical settings. The second aim is to determine if demographic factors among MHP's influence the extent to which they provide exercise recommendations as part of their treatment protocol. The third aim is to evaluate the effectiveness of a potential resource (Exercise Therapist) for the use of MHP's to facilitate exercise recommendations. Quantitative and qualitative questions were developed as part of a 26 item survey designed to identify the factors which encourage and discourage MHP's from recommending exercise. The participant population was comprised of 269 MHP's practicing in California. MHP's were most discouraged to recommend exercise because Exercise Therapy is outside their scope of practice. MHP's who regularly exercised were more inclined to recommend exercise than MHP's who do not regularly exercise. Other demographic variables (e.g., gender, age) did not influence exercise recommendations. Quantitative and qualitative data show support for Exercise Therapists as a resource to better treat depression. Further action by the MHF, government, and insurance agencies is needed to provide MHP's with the resources to more effectively use exercise to treat depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".