Analysis of the behavior change mechanism of township hospital health workers in Hubei Province, China
Bibliographic record
Abstract
This study aims to analyze the behavior changes of health workers in township hospitals by exploring their individual service, health information utilization, and health information exchange before and after intervention.A cross-sectional survey was conducted from September, 2016 to December, 2016 in Qianjiang city, Hubei Province, China. A total of 432 township hospital health workers were investigated from 12 township hospitals. t test and chi-square test were adopted in the difference analysis to compare the behavior changes and factors of the control and intervention groups before and after intervention. t test and U test were used to analyze the behaviors and the key impact factors of health workers in township hospitals. The hypothesis test of the behavior changes in the township hospitals were analyzed using the partial least squares (PLS) method.No significant difference was observed between the control and intervention groups of health workers in township hospitals. Significant differences were observed in the behavior attitude (BA), perceived behavior control (PBC), behavior intention (BI), and behaviors of information utilization and exchange in the intervention group. A significant difference was observed in the indicators of subjective norm (SN), BI, and behaviors with respect to information exchange. A large increment was observed in the intervention group. Based on results of PLS, the individual service, health information utilization, and health information exchange established relationships with BA, SN, PBC, and BI to a certain degree.A cause and effect relationship can be observed among BA, SN, PBC, BI, and behaviors of health workers in the township hospitals. BI can promote behavior changes among township hospital health workers. Moreover, different behaviors are demonstrated by different people because of BA, SN, PBC, and BI. The results of this study can contribute to improving the feasibility, pertinence, and effects of health service, and can serve as the guide in understanding health workers' behaviors.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".