Can managers be trained to further support their employees' basic needs and work engagement: A manager training program study
Bibliographic record
Abstract
Abstract The present paper describes a quasi‐experimental research presenting a workplace training program aimed at helping managers to be more supportive of their employees' autonomy. Drawing on self‐determination theory, we built a pre/post questionnaire design measuring perceived autonomy support, need satisfaction, need frustration, autonomous motivation, controlled motivation, work engagement and job burnout. Seven managers were trained according to the autonomy support training program. We assessed 39 of their employees before and after the intervention. Moreover, 133 employees whose managers were not included in the training program constituted the control group. Regarding the experimental group, the results showed significant statistical differences regarding perceived autonomy support from managers, autonomous motivation, need satisfaction, work engagement and job burnout. No significant effects regarding perceived autonomy support from coworkers controlled motivation, or need frustration were observed. This study provides added value to the theory of need satisfaction and demonstrates that training managers to be need‐supportive may be effective in improving positive work‐related outcomes and reducing negative outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".