Working with families: From theory to clinical nursing practice
Bibliographic record
Abstract
Aim: This qualitative study explored clinicians’ and educators’ perspectives on how knowledge and skills about family assessment and family nursing are translated from student learning to clinical nursing practice, together with barriers and supports for family-centred nursing practice. Background: Previous studies have explored educational preparation for family nursing and indicated that family-focussed nursing contributes to greater satisfaction with practice, however, little research has explored nurses’ perceptions about the usefulness of family nursing content and theory in clinical settings. Method: Data were collected from a Canadian school of nursing offering comprehensive undergraduate, postgraduate and staff development workshops in family nursing. Collection methods included participant observation in the school, a review of the school's teaching and learning documentation, and in-depth interviews/focus groups with teachers, students, graduates and workshop participants. Data were collected from 26 current students, undergraduate and postgraduate graduates, workshop participants and teachers from the school. Data were analysed for themes using grounded theory techniques of constant comparison and theoretical sampling. Findings: It was found that family nursing is more likely to be implemented in clinical practice areas where: patients experience serious or life-threatening illnesses, staff are educationally prepared, there is ongoing mentorship, and management support for family nursing. A family focus is less likely in areas with high patient turnover, such as acute medical-surgical wards. Conclusion: There is a need to adequately prepare nurses for family nursing, provide staff development and management support in the workplace to promote family-centred nursing practice.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".