Nature, stability and determinants of multi-target commitment profiles: a longitudinal person-centered approach
Bibliographic record
Abstract
Adopting a longitudinal person-centred perspective, we examined the profiles of employee commitments to the organization, supervisor, occupation, work team, and family in a diversified sample of employees (N = 1459) surveyed three times at one-month intervals during the COVID-19 pandemic. In line with recent developments in research on commitment towards multiple targets, these profiles were estimated while considering employees’ global levels of commitment to their work life (across targets) as well as the specific nature of their commitment to each target. Our results revealed six distinct commitment profiles differing quantitatively and qualitatively from one another and defined by employees’ global and target-specific levels of commitment. These profiles were replicated across the three measurement points. Profile membership was moderately to highly stable over time but also demonstrated some malleability. Lastly, we found that employees’ levels of basic psychological need satisfaction at work and perception of work meaningfulness predicted membership into more favourable commitment profiles.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".