Unpaid Informal Carers: The ‘Shadow’ Workforce in Health Care
Bibliographic record
Abstract
Introduction As Starr (1982) has observed, the 20th century marked the rise of scientific medicine and a shift in the site of care from people's homes to hospitals, which he characterised as ‘citadels of science’. While previously most people were born and died at home, now they are born and die in the hospital. The 21st century marks the reversal of that trend. As illnesses that can be cured on an episodic basis in hospitals are supplanted by multiple chronic health and social needs that cannot be cured, and must be managed over the long term, the big policy push across industrialised countries is to shift more care to community settings. Justifications include that, in ageing societies, growing numbers of older persons with ongoing care needs wish to ‘age at home’; hospital visits are costly; and lengthy hospital stays, particularly for frail older persons, can pose serious risks of hospitalborne illnesses and a progressive loss of functional capacity due to inactivity and lack of restorative care. This shift in the site of care also fundamentally changes the line-up of people who provide care. In hospitals, well paid, highly trained, highly regulated and highly visible professionals such as doctors, nurses, technologists and therapists deliver or direct most care. Beyond the hospital walls, these professionals – based on exclusionary social closure from a neo-Weberian perspective (Saks 2010) – have a more limited presence. Instead, non-professionalised personal support workers (PSWs), who are comparatively poorly paid, minimally trained, often unregulated and less visible, play a key role. In jurisdictions such as Ontario, Canada's largest province, PSWs account for most paid community-based support services such as personal care and homemaking. However, beyond these categories of paid workers exists another, largely uncharted, health human resource universe: untrained, unregulated and mostly invisible unpaid informal carers – the family, friends and neighbours who provide the bulk of everyday care required to support the wellbeing and independence of persons of all ages with ongoing health and social needs in community settings. As Lilly (2011) has commented, if paid health providers constitute the visible tip of the health care iceberg, unpaid informal carers constitute its submerged base.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 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".