Parent–nurse interactions: care of hospitalized children
Bibliographic record
Abstract
BACKGROUND: An essential component of quality nursing care is nurses' ability to work with parents in the hospital care of their children. However, changes in the health care environment have presented nurses with many new challenges, including meeting family-centred care expectations. AIM OF THE PAPER: To report a research study examining the experiences of parents who interacted with nurses in a hospital setting regarding the care of their children. METHODS: A qualitative approach was employed for this study. In-depth audiotaped interviews were conducted with eight parents representing seven families. Data collection was completed over a 7-month period in 2001. FINDINGS: Parents characterized their experiences with nurses caring for their children as interactions, and identified the elements of establishing rapport and sharing children's care as key to a positive perception of the interactions. These elements were influenced by parental expectations of nurses. Changes in nurses' approach were reported by parents as the children's conditions changed. CONCLUSION: Nurses were able to work with families in the hospital care of their children in ways that parents perceived as positive. However, in parents' views, their interactions with nurses did not constitute collaborative relationships. A deeper understanding of these interactions may provoke new thinking about how to promote an agency's philosophy, and how nurses enact this philosophy in 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".