Bibliographic record
Abstract
This thesis establishes a nationally representative sociodemographic profile of women in lowstatus care positions in long-term care (LTC) facilities across Canada, as well as assessing whether or not they experience a wages and wellness disadvantage, compared to non-care worker women. The thesis applies Paula England’s Devaluation Framework which suggests that women in positions of care work are typically rewarded less than comparable workers due to societal and cultural biases towards feminized fields of employment. Using 2016 Canadian Census micro-data accessed at the Prairie Regional Statistics Canada Research Data Centre, low-status care work is operationalized using occupation codes for Licensed Practical Nurses or Nurse’s Aides, Orderlies and Patient Service Associates alongside the industry code for Nursing and Residential Care Facilities. Using descriptive data analysis, the author identifies that low-status care work in LTC is overwhelmingly comprised of women, racialized individuals and foreign-born people in Canada. As well, using Ordinary Least Squares (OLS) Regression and Logistic Regression methods the author finds that women in low-status care occupations experience a wage penalty in comparison to all other employed women in Canada, with women of colour experiencing a double disadvantage in terms of wages. Lastly, this thesis demonstrates that low-status women care workers also experience a disadvantage in terms of both self-rated physical and mental health in comparison to women in other occupations. Several suggestions for future research and policy implications are explored based on these research findings such as fair remuneration, employment benefits, appropriate staffing levels, and unionization for care workers in LTC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".