Development and evolution of commitment profiles among military recruits: Implications for turnover intention and well-being
Bibliographic record
Abstract
We investigate the development and consequences of commitment profiles among Canadian Armed Forces (CAF) recruits who completed surveys at the end of basic training (N = 3998) and three (N = 636) and nine (N = 612) months later. The surveys included measures of affective, normative, and continuance commitment as well as measures developed by the CAF to assess recruits' experiences, career intentions, and well-being. Latent profile analyses of commitment at the end of basic training revealed four quantitatively distinct profiles (i.e., profiles differing in elevation but not shape). Strength of commitment related positively with perceived values fit, support from instructors and fellow recruits, and well-being, and negatively with turnover intention. Analyses of longitudinal data obtained following basic training revealed a stable and more differentiated 6-profile structure reflecting weak, exchange-based (continuance-dominant) and value-based (strong affective alone or in combination with strong normative and continuance) commitment. Value-based profiles were associated with greater perceived values fit, supervisor support, and well-being, and lower turnover intentions. The relative advantages of identifying the more nuanced commitment mind-sets reflected in commitment profiles is discussed along with the relevance of early onboarding experiences for the development of value-based commitment and retention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".