Nursing discharge teaching for hospitalized older people: A rapid realist review
Bibliographic record
Abstract
AIM: To develop, refine and put forward a programme theory that describes configurations between context, hidden mechanisms and outcomes of nursing discharge teaching. DESIGN: Rapid realist review guided by Pawson's recommendations and using the Realist and Meta-narrative Evidence Syntheses: Evolving Standards. DATA SOURCES: We performed searches in MEDLINE, Embase, CINAHL Full text, Google Scholarand supplementary searches in Google. We included all study designs and grey literature published between 1998-2019. REVIEW METHODS: We followed Pawson's recommended steps: initial programme theory development; literature search; document selection and appraisal; data extraction; analysis and synthesis process; presentation and dissemination of the revised programme theory. RESULTS: We included nine studies and a book to contribute to the synthesis. We developed 10 context-mechanisms-outcome configurations which cumulatively refined the initial programme theory. These configurations between context, mechanisms and outcome are classified in four categories as follows: relevancy of teaching content; patients' readiness to engage in the teaching-learning process; nurses' teaching skills and healthcare team approach to discharge teaching delivery. We also found that some of the same contexts generated similar outcomes, but through different mechanisms, highlighting interdependencies between context-mechanisms-outcome configurations. CONCLUSION: This rapid realist review resulted in an explanatory synthesis of how discharge teaching works to improve patient-centred outcomes. The proposed programme theory has direct implications for clinical practice by giving meaning to the 'hidden' mechanisms used by nurses when they prepare patients to be discharged home and can inform curricula for nursing education. IMPACT: The essential components, process mechanisms, contexts and impacts of the nursing discharge teaching are not consistently or clearly described, explained or evaluated for effectiveness. This review uncovers underlying contexts and mechanisms in the teaching/learning process between patients and nurses. The resulting programme theory can guide nurse clinicians and managers towards improvements in conducting discharge teaching.
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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 | 0.084 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".