<p>The APEC Digital Hub-WONCA Collaborative Framework on Integration of Mental Health into Primary Care in the Asia Pacific</p>
Bibliographic record
Abstract
Mental ill health affects individual well-being and national economic prosperity and makes up a substantial portion of the burden of disease globally, especially in the Asia-Pacific region. Integrating mental health into primary care is widely considered a key strategy to improve access to mental health care. Integration, however, is a complex process that needs to be addressed at multiple levels. A collaboration between the Asia-Pacific Economic Cooperation (APEC) Digital Hub for Mental Health and the World Organization of Family Doctors (WONCA) is described in this paper, which outlines a framework and next steps to improve the mental health of communities in APEC economies. This paper notes gaps related to the integration of mental health into primary care across the region and identifies enablers and current best practices from several APEC economies. The potential of digital technology to benefit primary mental health care for populations in the APEC region, including delivery of training programs for healthcare staff and access to resources for patients, is described. Finally, key next steps are proposed to promote enhanced integration into primary care and improve mental health care throughout the APEC region.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".