The impact of organizational commitment on job performance in primary healthcare: a motivation internalization perspective
Bibliographic record
Abstract
Introduction: Primary healthcare workers (PHCWs) are crucial to the healthcare system, as they directly impact the delivery of essential health services. Their job performance is influenced by various types of organizational commitment, but the effects of these commitments are not fully understood. This study aims to explore how four types of organizational commitment (affective, normative, economic, and opportunity) affect job performance among PHCWs, using Self-Determination Theory to examine motivation internalization as a mediating factor. Methods: A cross-sectional survey of 870 PHCWs from 38 primary healthcare institutions was conducted. Hierarchical regression analysis was used to explore the relationships between commitment types, motivation internalization, and job performance. Results: Affective and normative commitments positively predicted job performance, with motivation internalization partially mediating this relationship. Opportunity commitment negatively predicted job performance, mediated by reduced motivation internalization. Economic commitment showed no significant effect on either motivation internalization or job performance. Discussion: The impact of organizational commitment on job performance is shaped by its motivational quality. Strengthening affective and normative commitments through supportive incentive strategies can enhance PHCWs' performance in primary healthcare settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".