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Record W4401834907 · doi:10.3233/wor-240208

Challenges in teleworking management related to accommodations, inclusion, and the health of workers: A qualitative study through the lens of social exchanges

2024· article· en· W4401834907 on OpenAlexafffund
Alexandra Lecours, Roxanne Bédard-Mercier, Quan Nha Hong, Joanie Maclure, Claude Vincent, Marie-Michèle Lord

Bibliographic record

VenueWork · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversité LavalUniversité du Québec à Trois-RivièresUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersRéseau québécois de recherche sur le vieillissement
KeywordsInclusion (mineral)AccommodationWorkforceQualitative researchPublic relationsGerontologyBusinessNursingPsychologySociologyMedicinePolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.417
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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