Investigating the implications of changes in supervisor and organizational support.
Bibliographic record
Abstract
Workers tend to experience many benefits when they work for supportive supervisors and organizations. But what happens when workers experience changes in perceived support, more or less support than they typically experience? We studied family-supportive supervision (FSS) and perceived organizational support (POS) to test how changes in the perception of support in response to the COVID-19 pandemic may influence workers. Three waves of survey data from 368 workers in the United States and Canada were collected as the human and economic toll of COVID-19 manifested. Random-intercepts cross-lagged panel analyses were used to differentiate between stable associations and the within-person changes of interest. Stable associations among variables were consistent with prior research, but cross-lagged effects painted a complex picture that offered reasons for hope and concern. As hypothesized, we observed evidence for gain cycles such that there were reciprocal positive associations between FSS and POS, and higher-than-normal POS was associated with greater job satisfaction. However, remaining hypotheses were not supported, as changes in FSS and POS were not significantly associated with job insecurity, and heightened FSS was associated with higher levels of anxiety and depression. Our study reinforces prior findings by showing that employees generally benefit when working for supportive supervisors and organizations, while also suggesting that episodic changes in FSS and POS may have limited impacts on workers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".