Making a Better Living from Caregiving: Comparing Strategies to Improve Wages for Care Providers*
Bibliographic record
Abstract
Plusieurs stratégies ont été utilisées implicitement ou explicitement afin d'améliorer le taux de rémunération des prestataires de soins, pour la plupart des femmes. Ces tactiques comprennent: élever les titres de compétences requis pour exercer la profession; organiser et syndicaliser; exiger la parité salariale; solliciter du financement public pour leurs services. Alors que plusieurs chercheurs ont étudié le processus et les effets de ces stratégies sur les sciences infirmieres, très peu d'études semblables ont été réalisées auprès d'autres prestataires. Dans cet article, les auteures ont entrepris l'examen comparatif de l'efficacité des quatre stratégìes énumérées ci‐dessus pour atteindre les taux de rémunération appropriés en sciences infirmières, dans la profession de sage‐femme et en puèriculture. Several strategies have been employed either implicitly or explicitly to improve the remuneration levels of largely female care providers; these tactics include increasing entry‐to‐practice credentials; organizing and unionizing; seeking pay equity considerations; and seeking public funding for their services. While many scholars have investigated the process and effects of these strategies for nursing, there has been very little similar research conducted concerning other care providers. In this paper, we undertake a comparative examination of the effectiveness of the four aforementioned strategies in achieving appropriate levels of remuneration within nursing, midwifery and child care.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".