Trajectories of Affective Organizational and Occupational Commitment: The Case of Public Service Employees
Bibliographic record
Abstract
The goal of this dissertation is to document the evolution of affective organizational and occupational commitment trajectories among public service employees. To this end, three longitudinal person-centered studies were conducted to identify the main types of commitment trajectories identified among three independent samples of participants (i.e., school principals, nurses, military recruits), and to assess how these trajectories were related to a variety of antecedents and outcomes. A first study focused on the occupational commitment trajectories of 661 established school principals (42% males) followed over a period of two years. A second study focused on the organizational commitment trajectories of 4859 military recruits (68.4% males) followed across basic training (3 months) and their first nine months of employment in the Canadian Armed Forces. A third study had a dual focus on the organizational and occupational commitment of 659 early career nurses (12% males), recruited within their first three year of employment, and followed over the course of two years (allowing us to estimate trajectories covering their first five years in the nursing occupation). All three studies identified profiles of employees following persistently high commitment trajectories, persistently low or decreasing commitment trajectories (both of which were identified among school principals) and increasing commitment trajectories. Among school principals and nurses, a persistently moderate commitment trajectory was also identified. Moreover, our results demonstrated the benefits of efficient socialization practices (military recruits, nurses), basic psychological need fulfillment (school principals, nurses), realistic job previews (military recruits) and satisfaction with the implications of military life for work-life balance (military recruits), as well as the harmful nature of experiencing identity conflicts (military recruits). Finally, our results demonstrated the benefits of higher and increasing commitment trajectories for a variety of outcomes, including lower levels of burnout (school principals), psychological distress (nurses), psychosomatic symptoms (nurses), turnover intention (school principals, military recruits), transition intention (military recruits), and higher levels of satisfaction (school principals, military recruits, nurses) and quality of care (nurses). These results suggest multiple avenues to foster desirable commitment trajectories and its associated benefits, which will be highlighted in each of the chapters as well as in the general discussion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".