Concilier travail et soins à un proche au Canada: quel soutien des acteurs communautaire ?
Bibliographic record
Abstract
S’il peut être satisfaisant de prendre soin d’un proche en perte d’autonomie, le rôle de proche-aidant engendre un certain nombre de tensions et des difficultés à se maintenir en emploi. Face à cette situation, les acteurs communautaires ont été désignés comme une source de soutien importante pour les proches-aidants. Il n’existe toutefois à notre connaissance aucune étude qualitative se penchant sur la relation de soutien entre organismes communautaires et proches-aidants en emploi. Suite à notre recherche comprenant 33 entrevues semi-directives auprès de proches-aidants actifs sur le marché du travail et d’organismes communautaires québécois, menée entre septembre 2014 et juin 2015 au Québec, nous constatons deux faits majeurs : 1) les acteurs communautaires ont le potentiel de constituer une réelle ressource pour concilier emploi et soins à un proche; 2) par contre, leurs services tendent à être méconnus et difficilement accessibles par la majorité des employés proches-aidants. However rewarding taking care of a relative can be, to be a caregiver causes multiple tensions and difficulties in remaining employed. In view of this issue, community organizations have been referred to as a great source of support for caregivers. However, to our knowledge, no qualitative study has ever focussed on the supportive relationship between the community sector and employed caregivers. Our study involved 33 semi-directive interviews with working caregivers and community organizations in Quebec between September 2014 and June 2015. It revealed two main facts: 1) community organizations have the potential to be a real source of support for work-care balance; 2) however, their services remain unknown and hard to access by the majority of employed caregivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".