Understanding Practices of Patient and Family Centred Care in Hospitals
Bibliographic record
Abstract
In Canada, the adoption of Patient and Family Centred Care (PFCC) initiatives for adult patient populations across hospital care settings are thought to improve quality of care, safety, and cost effectiveness. However, PFCC strategies have been reported to be difficult to implement and sustain at the direct level of acute patient care due to a variety of issues including competing organizational priorities, paradoxical ethical implications, and insufficient conceptual knowledge about PFCC in practice. These persistent difficulties suggest that care in general may actually be more complex than often assumed. As such, this study investigates everyday care practices to explore how patient and family centredness is enacted in acute care. The main research objective of this study is to think about the possibilities of improving care for patients and families in hospital settings by paying attention to the complex and entangled relations, actors, technologies, and politics involved in organizing care. A practice view of work and organization is used to explore how care of families and patients is enacted in hospitals. Ethnographic methods, including participant observation, document analysis, and interviews with patients, families and members of patient care teams were conducted over one year. The study took place in a teaching hospital located within Alberta and included two surgical wards that specialized in the delivery of orthopaedic care to adult patient populations. Five patients and their family members were recruited as case study participants. Nurses and unit managers were also shadowed, interviewed, and observed while organizing everyday care. Findings in this study are organized around three observations. First, hospitals are not neutral settings of care in which PFCC can simply be implemented. Second, there were multiple values entangled in nursing practices and these were not always aligned with the values, desires, or preferences of patients. Third, despite, or perhaps because of, significant material and bodily precarity, patients and families in this study worked actively to ‘centre’ themselves in day-to-day orthopaedic care practices. These findings are relevant for nurses and researchers interested in reinventing ideas about good care practices for patients and families experiencing hospitalization.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.009 |
| 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".