Bibliographic record
Abstract
AIM: The paper is a report of a study conducted to evaluate the effect of a brief, focused educational intervention on the quality of verbal interactions between nursing staff and patients in a chronic care facility. BACKGROUND: Positive nurse-patient communication in chronic care is crucial to the quality of life and well-being of patients. Despite this, patients are dissatisfied with these interactions and nursing staff indicate the need for additional training. METHOD: A repeated-measures design was used to collect data between April 2003 and February 2004, by audiotaping verbal interactions between nursing staff and patients during morning care. Baseline data were analysed and an educational intervention was developed based on the results of the pretest. Five months after the educational intervention, interactions between the same nursing staff and patients were audiotaped. Twenty-seven randomly chosen patients and selected nursing staff participated in the study. Data were analysed using a qualitative comparative method, and a quantification technique was developed to compare the quality of the interactions before and after the intervention. FINDINGS: Preintervention interactions were task-oriented, superficial and dominated by nursing staff. Results statistically significantly improved after the intervention was implemented. Nursing staff were less authoritative, used more solution-focused communication and interactions had a statistically significantly higher positive ratio. CONCLUSION: Brief interventions can change nursing staff's communication practice and they realized the importance of effective communication as a fundamental component to deliver patient-focused care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".