Primary care provider's job satisfaction and organizational commitment after COVID-19 restrictions ended: A mixed-method study using a mediation model
Bibliographic record
Abstract
Objectives More and more countries have decided to cancel most or even all COVD-19 restrictions. However, it is unclear how ending of restrictions will affect primary care providers' job satisfaction and organizational commitment. Our objectives are to explore the current status and possible change in primary care providers' job satisfaction and organizational commitment after massive restriction policies ended in China. Methods This was a mixed-method study that utilized structured questionnaires and semi-structured qualitative individual interviews. The 20-item Minnesota Satisfaction Questionnaire (MSQ) and 25-item organizational commitment survey were adopted to assess job satisfaction and organization commitment. Descriptive statistics and mediation models, as well as inductive thematic analysis, were used to analyze quantitative and qualitative data. Results A total of 18 interviews and 435 valid survey responses were included in our analysis. The average scores for job satisfaction and organizational commitment were 80.6 and 90.8. The thematic analysis revealed one major theme: ethical and moral responsibility to provide care as primary care providers, on which we established a mediation model. The mediation analysis revealed that normative commitment could positively affect the other four dimensions of organizational commitment and job satisfaction. The direct effect of affective commitment on job satisfaction was significant (LLCI = 0.11, ULCI = 0.31), and the mediators were identified to have a partial mediating effect instead of a total mediating effect. Conclusion After COVID-19 restrictions end, the job satisfaction and organizational commitment of primary care providers will return to levels before the pandemic and during this estimated process, a brief rise in resignation is predictable. The normative commitment positively affects the other four dimensions of organizational commitment and job satisfaction for primary care providers, which suggests a possible way to motivate primary care providers when restrictions end.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".