The Social Relations of Home Care Nursing Work
Bibliographic record
Abstract
There is an increasing need for home care in Canada; however, there is little evidence about the everyday nursing work in home care and the institutional influences that impact this work. As nurses are the largest professional care providers in home care and given the increasing demands for home care services, there is a need to understand the work of nurses, specifically to identify the social organization of this work. As a part of a larger Canadian study on home care systems, this institutional ethnography focused on home care nurses in one health authority in Western Canada. The standpoint of nurses was explored through interviews, observations, and collected texts used to explicate the social relations coordinating home care nursing work. The results of this inquiry show that nurses’ work is coordinated through texts and electronic health documentation systems. Safety, measurement, and efficiency are shown to influence nurses’ work. Alongside the discursive arrangements, increasingly nurses’ time coordinating their work and client care is expanding, with less time for direct client care. To meet the increasing demand for home care, insight is needed to improve access and care. Understanding the invisible but dominant ruling relations organizing influencing, and at times disorganizing, the everyday work of nurses is a vital first step in creating change for home care nursing.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.054 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".