A community–academic partnership develops a more responsive model to providing depression care to disadvantaged adults in the US
Bibliographic record
Abstract
BACKGROUND: Socioeconomically disadvantaged adults experience greater healthcare disparities and increased risk of depression compared to higher-income groups. AIM: To create a depression care model for disadvantaged adults utilizing service agencies, through a community-academic partnership. METHODS: Using participatory research methods, an organizational needs assessment was performed to ascertain depression care needs, identify barriers to clients receiving treatment, and marshal resources. Interviews and surveys were conducted with community organizational leaders. Focus groups were conducted with clients who used the service agencies. RESULTS: Interviews and surveys identified barriers including discontinuity of care and unmet basic needs for food, housing, health insurance and transportation. Focus groups enriched the understanding of barriers including lack of motivation to seek depression care, lack of social support and needed resources for the uninsured, underinsured and homeless. The findings were used to develop a depression care model combining depression management with motivational interviewing to evaluate and meet needs, and peer education to motivate and provide support. CONCLUSIONS: This partnership facilitated the development of a community-driven intervention that academic researchers acting alone could not realize. To provide depression care to socioeconomically disadvantaged individuals, the intervention must include mitigating solutions to barriers.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".