Transitions in Labour Force Participation over the Palliative Care Trajectory
Bibliographic record
Abstract
Background: Home-based palliative programs rely on family caregivers, who often miss time from employment.This article identified changes in caregivers' labour force participation over the palliative trajectory.Methods: Family caregivers (n = 262) were interviewed biweekly to measure transitions across four employment categories.[26] HEALTHCARE POLICY Vol.16 No.2, 2020 Results: More than half of the caregivers had one employment transition and 29% had three or more.The highest proportion of transitions occurred for caregivers who were employed part-time.Interpretation: Understanding these transitions is critical to the development of strategies tailored to caregivers to contain labour force losses and to support caregivers during a time of high caregiving demands. RésuméContexte : Les programmes de soins palliatifs à domicile comptent sur la contribution des proches aidants qui, souvent, doivent s' absenter du travail.Cet article identifie les changements dans l' activité de la main-d' œuvre au cours de la trajectoire des soins palliatifs.Méthode : Des proches aidants (n = 262) ont été interviewés au deux semaines afin de mesurer les transitions dans quatre catégories d' emploi.Résultats : Plus de la moitié des proches aidants ont connu une transition d' emploi et 29 % en ont connu trois ou plus.La plus forte proportion de transition touche les proches aidants qui occupent un emploi à temps partiel.Interprétation : La compréhension de ces transitions est essentielle pour le développement de stratégies sur mesure pour les proches aidants afin de freiner la perte de main-d' œuvre et d' aider les proches aidants en période de grande demande pour ce type d' aide.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".