Governing families that care for a sick relative: the contributions of Donzelot’s theory for nursing
Bibliographic record
Abstract
According to the literature, the family is now considered to be the most important resource for the care and support of a sick family member. Families are being increasingly invited and trained to play a utilitarian role, not just as family caregivers, but as healthcare agents. Healthcare institutions, based on neoliberal health policies, are encouraging them to perform increasingly complex and professionalized tasks. The burden associated with this expanded healthcare function, however, is significant (fatigue, emotional distress and exhaustion). The aim of this article was to present French sociologist Jacques Donzelot's theoretical perspective on governing through the family. According to Donzelot, such a government is exercised through various power techniques, including the instrumentalization of the family role and the transfer to families of the responsibility for health care. This author describes how healthcare institutions call on the family to perform hospital and biomedical practices within the home. A spin-off of neoliberalism, the practices of governing through families specifically target women, who are considered to be the pillar of the family. Donzelot's perspective is very relevant to nursing, but is still rarely mobilized in the discipline. His critical perspective allows for a re-reading of relations of power and mechanisms of surveillance and control of families, issues that are often overlooked in nursing research.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".