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Record W7039267147

Les facteurs favorables à la rétention en contexte de télétravail dans le secteur des technologies de l'information au Québec

2025· other· fr· W7039267147 on OpenAlexaboutno aff

Bibliographic record

VenueSémaphore (Université du Québec à Rimouski) · 2025
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)LimitingWork (physics)Long-term prediction
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ : Cette recherche se concentre sur la rétention des employés dans le secteur des technologies de l’information du Québec, spécifiquement dans un cadre de télétravail exclusif. Plus précisément, l’objectif de ce mémoire est d’explorer, d’identifier et de décrire les facteurs de rétention des employés du secteur des technologies de l’information en contexte exclusif de télétravail au Québec. La pandémie de COVID-19, survenue en 2020, a marqué un tournant décisif en instaurant le télétravail exclusif comme norme organisationnelle dans plusieurs milieux. Il s’agit dès lors de s’intéresser à l’influence que cette situation a pu avoir sur la rétention organisationnelle. Le cadre méthodologique adopté pour cette étude est de nature qualitative, orienté vers une approche exploratoire. L’étude repose sur la tenue de groupes de discussion avec des employés œuvrant dans le domaine des technologies de l’information et des communications. Trois groupes de discussion, totalisant un échantillon de 22 personnes, ont été organisés pour identifier les facteurs de rétention dans un cadre de télétravail exclusif. Au cours de ces discussions, les participants ont partagé leurs expériences en matière d’intégration, de collaboration en équipe et de relations avec leurs gestionnaires. Parmi les principaux facteurs de rétention identifiés se trouvent la flexibilité offerte par le télétravail, la conciliation travail-vie personnelle, le soutien entre collègues et une culture d’entraide au sein des équipes. Les défis évoqués incluent le manque de contact humain, les difficultés de communication, ainsi que la nécessité d’améliorer le soutien à l’intégration et à la formation des nouveaux employés. -- Mot(s) clé(s) en français : rétention organisationnelle, télétravail, technologie de l’information et des communications, gestion. -- ABSTRACT :This research focuses on employee retention in the information technology (IT) sector, specifically in the context of exclusive telecommuting in Quebec. More precisely, the aim of this thesis is to explore, identify, and describe the factors influencing employee retention in the IT sector within the context of exclusive telecommuting in Quebec. The COVID-19 pandemic, which occurred in 2020, marked a decisive turning point by establishing exclusive telecommuting as an organizational norm in many workplaces. Therefore, this study seeks to examine the impact that this situation may have had on organizational retention. The methodological framework adopted for this study is qualitative, with an exploratory approach. The study relies on focus groups with employees working in the field of information and communication technologies (ICT). Three focus groups, totaling a sample of 22 people, were organized to identify retention factors in the context of exclusive telecommuting. During these discussions, participants shared their experiences regarding integration, team collaboration, and relationships with their managers. Among the main retention factors identified were the flexibility offered by telecommuting, work-life balance, peer support, and a culture of mutual assistance within teams. The challenges mentioned included the lack of human contact, communication difficulties, and the need to improve support for the integration and training of new employees. -- Mot(s) clé(s) en anglais : organizational retention, telecommuting, information and communication technology, management.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.438

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.003
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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