Self-determination as a moderator of demands and control : implications for employee strain and engagement
Bibliographic record
Abstract
Does job control act as a stress-buffer when employees' type and level of work self-determination is taken into account? It was anticipated that job control would only be stress-buffering for employees high in self-determined and low in non-self-determined work motivation. In contrast, job control would be stress-exacerbating for employees who were low in self-determined and high in non-self-determined work motivation. Employees of a health insurance organization (N = 123) completed a survey on perceptions of role overload, job control, work self-determination, and a range of strain and engagement indicators. Results revealed that, when individuals high in self-determination perceived high job control, they experienced greater engagement (in the form of dedication to their work). In addition, when individuals high in non-self-determination perceived high job demands, they experienced more health complaints. A significant 3-way interaction demonstrated that, for individuals low in non-self-determination, high job control had the anticipated stress-buffering effect on engagement (in the form of absorption in their work). In addition, low job control was stress-exacerbating. However, contrary to expectations, for those high in non-self-determination, high job control was just as useful as low job control as a stress-buffer. The practical applications of these findings to the organizational context are discussed.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".