Research on Problems and Countermeasures of Health Records in Community Health Services in Tai'an City
Bibliographic record
Abstract
<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Objective: To standardize and improve the management of health records in community health services in Tai’an city, also to improve community health service level, and enhance residents' awareness of health. Method: taking the residents within the service range of TaiQian Community Health Center in Taian city and the relevant medical staff as research respondent, 120 questionnaires have been sent out to the residents and 15 questionnaires to the staff, then statistical analysis would be made according to the survey results. Results: at current, there are two main ways to establish personal health records, i.e. residents health examination and community personnel pay visit, which are effective; community residents seldom use their personal records, only 93.5% for once a year; 35% of community doctor hold the opinion that the health records have no big value, so there is no need to read, reflecting that the community medical personnel lack of the awareness of use it; the security and completeness of electronic health records are poor. Conclusion: Government should play a leading role in establishing and managing the community health record, improve awareness of community medical personnel in using health records through training, seminars, On-line advertising and other forms, and strengthen and improve the comprehensive management of electronic health records.</span></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".