ARE BAD JOBS INEVITABLE? A COMPARATIVE STUDY OF AGED-CARE OCCUPATIONS AND TRAINING
Bibliographic record
Abstract
As the population ages globally, the care of older adults with chronic disease and physical and cognitive impairment will require a significantly larger and more skilled direct care workforce than currently exists. Many countries have migration policies to encourage workers to leave home and perform this important work. Others relegate the majority of the labor to vulnerable populations including women of color and immigrants. Do these jobs have to be “bad jobs” with poor compensation, few benefits and heavy workloads? This study comparatively examines the demographic composition and extrinsic characteristics of entry-level aged care jobs in select countries: U.S., Canada, United Kingdom, Australia, Denmark, Norway and China, paying particular attention to the social welfare, immigration and training policies for workers in home and community based settings. Data for this study come from country-specific interviews with the aging and employment context experts and publically available data. Findings indicate there are similarities across these countries in the increasing demand for workers in both institutional and residential settings, particularly in localities with older and more rural populations. There is wide variability across countries in training pathways for aged care workers, in the structure of these programs (i.e. from on the job to 36 month long training requirements) as well as in specific competency areas (e.g. basic healthcare skills, observations and recordings, emergency training, communication and dementia-specific care). Social welfare policies have significant impact on the extrinsic rewards of these jobs across country setting. Implications for recruitment, retention, and social policy will be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".