More Than a Framework? Applying Socioecological Models Within Mental Health and Community Psychology Inquiries
Bibliographic record
Abstract
Socioecological models are commonly used by researchers, practitioners, and educators in mental health because these models acknowledge the environmental influences of well-being and help to inform the development of multipronged intervention strategies. However, their versatility and variety of uses in mental health and community psychology have presented challenges for researchers in defining the boundaries and acceptability of socioecological approaches. In this commentary, we provide an overview of socioecological approaches, describe some common applications, provide guidance for investigators seeking to use these models, and call for greater transparency in reporting to enhance its use in community psychology and mental health inquiries.
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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 | 0.109 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.100 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.016 |
| 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".