Personal Support Workers and the Labour Market
Bibliographic record
Abstract
Introduction Personal support workers (PSWs) represent a significant portion of the health care labour force in many countries. Using the example of Canada, this chapter focuses on the PSW labour market and its relationship to supply characteristics. Since the PSW labour market is situated within the broader health care labour market, in which professional and occupational dynamics, competition among various occupations and related hierarchies, and professional exclusionary closure operate (Saks 2010), it is considered within this context, in line with a neo-Weberian approach. PSWs are generally best classified under the working class in accordance with Weberian theory, wherein their key asset is labour power as opposed to formal credentials (Weber 1968). Given their shared position within this larger health care labour market, individuals making up the PSW labour force are likely to possess common traits and face similar barriers to economic mobility opportunities. Such shared characteristics among PSWs may lead to the inability to secure employment in other more valued (monetary and otherwise) health care occupations with higher educational requirements (such as nursing), resulting in PSW employment as a default option for some of these individuals. We largely focus on the Canadian PSW labour market in this chapter, but such factors apply to many PSWs in a wider global context. This chapter begins with a general description of the importance of the PSW labour market, and includes a definition of PSWs. It explores past, current, and forecasted supply and demand. Key contextual factors, including regulatory models in Canada, are noted. Since the data suggests that the PSW labour market is better thought of as a series of sub-markets which tend to differ along the lines of such factors as wages and hours worked, the analysis includes differences by sector across a multitude of individual and job characteristics, as well as labour market outcomes. It then examines individual characteristics such as socio-demographics (gender, age, marital status, immigrant status, visible minority status, ethnicity, Aboriginal status), family and household characteristics (informal care giving variables, family member employment), health status (self-reported health, injury rates and severity, disability, medical conditions), and education and training variables (highest level of education, relevance of education, years of experience).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".