Self-determination, control, and reactions to changes in workload: A work simulation.
Bibliographic record
Abstract
The objective of this experimental study is to capture the dynamic temporal processes that occur in changing work settings and to test how work control and individuals' motivational predispositions interact to predict reactions to these changes. To this aim, we examine the moderating effects of global self-determined and non-self-determined motivation, at different levels of work control, on participants' adaptation and stress reactivity to changes in workload during four trials of an inbox activity. Workload was increased or decreased at Trial 3, and adaptation to this change was examined via fluctuations in anxiety, coping, motivation, and performance. In support of the hypotheses, results revealed that, for non-self-determined individuals, low work control was stress-buffering and high work control was stress-exacerbating when predicting anxiety and intrinsic motivation. In contrast, for self-determined individuals, high work control facilitated the adaptive use of planning coping in response to a change in workload. Overall, this pattern of results demonstrates that, while high work control was anxiety-provoking and demotivating for non-self-determined individuals, self-determined individuals used high work control to implement an adaptive antecedent-focused emotion regulation strategy (i.e., planning coping) to meet situational demands. Other interactive effects of global motivation emerged on anxiety, active coping, and task performance. These results and their practical implications 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".