Challenges in teleworking management related to accommodations, inclusion, and the health of workers: A qualitative study through the lens of social exchanges
Bibliographic record
Abstract
BACKGROUND: Telework is increasingly prevalent and holds the potential to serve as an accommodation, facilitating inclusion and promoting healthy participation among various segments of the workforce, such as aging employees, individuals with chronic illnesses or those living alone with one or more dependents. Nevertheless, this promising avenue presents management challenges that remain underexplored in the literature. OBJECTIVE: This study aimed to identify the challenges in telework management related to accommodations, inclusion and the health of workers with life situations entailing specific needs. METHODS: We conducted a descriptive interpretative study grounded in Social Exchange Theory, by collecting data through interviews with 9 managers and conducting focus groups involving 16 workers. We used a thematic-analysis approach to analyze the data. RESULTS: We identified seven overarching themes encapsulating management challenges that relate to accommodation (e.g., maintaining a balance between the benefits for the worker and the impacts on the organization) inclusion (e.g., maintaining team cohesion) and health (e.g., managing teleworkers' emotions). CONCLUSIONS: The findings underscore the significance of fostering robust social exchanges across hierarchical levels, and they highlight the necessity of equipping managers with the requisite tools to navigate the ethical quandaries arising from accommodation requests.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".