Wages or Legitimacy? A Qualitative Analysis of Home Care Worker Perspectives in Choosing Work Settings
Bibliographic record
Abstract
Directly-Funded (DF) home care allows users to organize and purchase their own care services and is expanding globally. Little is known about the career pathways of home care workers. Our study asks, what experiences and factors do home care workers consider when choosing a work setting? And, specifically, what influences their decisions to work directly for their clients? Framed with Cranford's (2020) flexibility-security matrix for analyzing home care dynamics, we remotely interviewed 20 home care workers in two Canadian provinces. Three team members conducted axial coding and thematic analysis using Dedoose software. We identified personal and material factors at the intimate and labor market level that workers weigh when choosing whether to work for an agency or directly for a client. At the intimate level, workers value the flexibility, autonomy, and respect facilitated in care relations when working directly for a client. At the labor market level, agencies provide better job security and the benefit of supervisory support but lower wages. Additionally, as care work often serves as a stepping stone for immigration and citizenship agency positions are considered a more "legitimate" option than working directly for a client. Our study shows that workers directly employed by their clients enjoy more flexibility but lack security, whereas agency employed workers risk immediate reductions in working conditions in exchange for limited improvements in safety and supervision and, like other frontline care work, DF home care represents a key career pathway for immigrants with previous experience in health and social care settings.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| 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".