Current Research Situation of Chinese Psychological Health over Recent Twenty Years
Bibliographic record
Abstract
Analysis was carried out on 538 papers about psychological health study (from 1998 to 2007) and which was included by CNKI (China National Knowledge Internet) data base using literature metrological method. The results showed: (1) the number of papers about Chinese psychological health study published in dominated academic Publications wasn’t large, the quality of which needs to be improved. (2) Concerning on the objects of study, more attention has been paid to teenagers’ psychological health study, and much less to pupils, preschool children, middle and old aged folks’ psychological health. (3) With regard of the research field, they concentrated on the relative factors about psychological health and the survey of psychological health’s current situation, and a few about the drawing of psychological health scale and psychological health education. (4) Cooperative research has been the leading way of psychological health study, but most of which were co operations inside one organization, the cooperations over institutions need to be strengthened. The team of psychological health study was forming, but it wasn’t stable, and there was not any core author. (5) The distribution of study power institutions focused on higher educational normal universities and mechanic colleges, hospitals, the distribution of study power regions mainly concentrated economic developed regions. Key words : China; Psychological Health; Metrology
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".