Analyzing Residents’ Health Information Requirement and Intention for the Library of Medical University and Its Influencing Factors
Bibliographic record
Abstract
Objective To investigate the residents’ requirement for health information in a library of medical university and its influencing factors to provide suggestions for health information service and activities to popularize scientific knowledge. Methods Convenience sampling method was used in the study to select some residents in 2 communities of ShaPingba for a face-to-face questionnaire survey. χ 2 test and logistic regression were used to analysis data by SPSS v25.0 software. Results Among the 317 residents, the contents of health information that individuals preferred included 70.7% for disease prevention and health care,81.1% for disease preservation and rehabilitation,37.2% for diagnosis and treatment of disease,6.6% for medical policies and regulations,5.4% for drugs information,5.0% for preclinical medicine. The way of health information that persons preferred contained 16.4% for newspaper and journals reading services,22.1% for book loan services,69.4% for training and lecture services,45.8% for information inquiry service. Multivariate logistic regression results showed that age was a significant factor influencing the status of health information contents. Age and education were significant factors influencing the status of health information ways. Conclusion Residents’ health information requirement is affected mainly by age and education. So it is essential to take the dominated influencing factors into consideration for spreading health information by various ways and channels when libraries prepare health information service and activities to popularize scientific knowledge, especially, more concerns should be put on practicability, maneuverability and accessibility of health information.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".