Identifying the competencies of doctors in China
Bibliographic record
Abstract
BACKGROUND: China adopted a Flexnerian model as its medical institutions developed over the recent past but the political, social, and economic environment has changed significantly since then. This has generated the need for educational reform, which in other countries, has largely been driven by competencies-oriented models such as those developed in Canada, and the United States. Our study sought to establish the competencies model, relevant to China, which will support educational reform efforts. METHODS: Data was collected using a cross-sectional survey of 1776 doctors from seven provinces in China. The surveys were translated and adapted from the Occupational Information Network General Work Activity questionnaire (O*NET-GWA) and Work Style questionnaire (O*NET-WS) developed under the auspices of the US Department of Labor. Exploratory factor analysis and confirmatory factor analysis ascertained the latent dimensions of the questionnaires, as well as the factor structures of the competencies model for the Chinese doctors. RESULTS: In exploratory factor analysis, the questionnaires were able to account for 64.25 % of total variance. All responses had high internal consistency and reliability. In confirmatory factor analysis, the loadings of six constructs were between 0.53 ~ 0.89 and were significant, Construct reliability (CR) were between 0.79 ~ 0.93 respectively. The results showed good convergent validity. The resultant models fit the data well (GFI was 0.92, RMSEA was 0.07) and the six-factor competencies framework for Chinese doctors emerged. CONCLUSIONS: The Chinese doctors' competencies framework includes six elements: (a) technical procedural skills; (b) diagnosis and management; (c) teamwork and administration; (d) communication; (e) professional behavior; and (f) professional values. These findings are relevant to China, consistent with its current situation, and similar to those developed in other countries.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".