Child and adolescent psychiatry in the Far East: A 5‐year follow up on the Consortium on Academic Child and Adolescent Psychiatry in the Far East (CACAP‐FE) study
Bibliographic record
Abstract
AIM: Data pertaining to child and adolescent psychiatry (CAP) training systems are limited as extant research has mostly been derived from one-time data collection. This 5-year follow-up survey collects updated information on CAP training systems in the Far East, allowing for the tracking of system changes over the past 5 years. METHODS: Data were obtained from 18 countries, or functionally self-governing areas, in the Far East, 17 of which were also included in the original study. An online questionnaire was completed by leading CAP professionals in each country. Questions were expanded in the present study to capture the contents of CAP training. RESULTS: When compared to data from the original study, there has been progress in CAP training systems in the last 5 years. Specifically, there has been an increase in the number of countries with CAP training programs and national guidelines for the training. In addition, the number of CAP departments/divisions affiliated with academic institutions/universities has increased. Findings from 12 of 18 countries in the present study provide data on clinical contents. All informants of the present study reported the need for more child and adolescent psychiatrists and allied professionals. CONCLUSION: Despite progress in CAP training systems over the last 5 years, the need for more professionals in child and adolescent mental health care in all the relevant areas in this region have yet to be adequately addressed. Continued national efforts and international collaborations are imperative to developing and sustaining new CAP training systems while facilitating improvements in existing programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".