Key organizational commitment antecedents for nurses, paramedical professionals and non‐clinical staff
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to develop a causal model that explains the antecedents and mediating factors predicting the organizational commitment of healthcare employees in different work roles. DESIGN/METHODOLOGY/APPROACH: This study tests an integrative causal model that consists of a number of direct and indirect relationships for antecedents of organizational commitment. It is proposed that the relationship between job satisfaction and organizational commitment is best understood by focusing on the three interrelated facets of job satisfaction, i.e. satisfaction with career advancement, satisfaction with supervisor, and satisfaction with co-workers. However, the model also advances that these job satisfaction facets have different mediating effects for other antecedents of organizational commitment. FINDINGS: The Structural Equation Modeling (SEM) path analysis showed that the job satisfaction facets of career advancement and satisfaction with supervisor had a direct impact on organizational commitment. Employee empowerment, job-motivating potential, effective leadership, acceptance by co-workers, role ambiguity and role conflict were also important determinants of organizational commitment. Interestingly, post hoc analyses showed that satisfaction with co-workers only had an indirect impact on organizational commitment. ORIGINALITY/VALUE: While there has been extensive research on organizational commitment and its antecedents in healthcare organizations, most previous studies have been limited either to a single employee group or to a single time frame. This study proposes a practical causal model of antecedents of organizational commitment that tests relationships across time and across different healthcare employee groups.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".