Strategies Used by Middle Managers to Support Employees Carrying Out Emotionally Demanding Work: Which Strategies and Why Those Ones?
Bibliographic record
Abstract
Many areas of practice in health and social services are emotionally demanding. This type of work can be associated with psychological health problems and middle managers play a key role in reducing such risks for their staff. Although the importance of providing this support is recognized, attaining such an objective is not necessarily straightforward because of the multiple demands that managers must juggle. Using an ergonomic perspective, this qualitative research study, which was conducted in a regional child protection service in Quebec (Canada), aimed to identify the strategies used by middle managers to support staff whose work is considered emotionally demanding. The results reveal that managers use a range of support strategies, which fall into seven categories. Although the strategies are distributed along two axes, proximity (direct, indirect) and time (short-term, long-term), they tend to be more direct and short-term (e.g., provide emotional support). The choice of strategies is influenced by various facilitating or constraining organizational, interpersonal and individual factors. A strong influence appears to be time availability. This study provides a detailed picture of the strategies used by middle man-agers and the complexity with which these individuals are confronted in providing their staff with support. Further research is required, for example, to better understand the impact of certain factors on the choice of support strategies and to evaluate the impact of support strategies from a staff perspective.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".