Patient experiences of in‐hospital preparations for follow‐up care at home
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine patient experiences of hospital-based discharge preparation for referral for follow-up home care services. To identify aspects of discharge preparation that will assist patients with their transition from hospital-based care to home-based follow-up care. BACKGROUND: To improve patients' transitions from hospital-based care to community-based home care, hospitals incorporate home care referral processes into discharge planning. This includes patient preparation for follow-up home care services. While there is evidence to support that such preparation needs to be more patient-centred to be effective, there is little knowledge of patient experiences of preparation that would guide improvements. DESIGN: Qualitative descriptive study. METHODS: The study was carried out at a supra-regional hospital in Eastern Canada. Findings are based on thematic content analysis of 13 semi-structured interviews of patients requiring home care after hospitalisation on a medical or surgical unit. Most interviews were held within one week of discharge. RESULTS: Patient experiences were associated with patient attitudes and levels of engagement in preparation. Attitudes and levels of engagement were seen as related to one another. Those who 'didn't really think about it', had low engagement, while those with the attitude 'guide me', looked for partnership. Those who had an attitude of 'this is what I want', had a very high level of engagement. CONCLUSIONS: Previous experience with home care services influenced patients' level of trust in the health care system, and ultimately shaped their attitudes towards and levels of engagement in preparation. RELEVANCE TO CLINICAL PRACTICE: Patient preparation for follow-up home care can be improved by assessing their knowledge of and previous experiences with home care. Patients recognised as using a proactive approach may be highly vulnerable.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".