Examining the course of transitions from hospital to home-based palliative care: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: Hospital-to-home transitions in palliative care are fraught with challenges. To assess transitions researchers have used patient reported outcome measures and qualitative data to give unique insights into a phenomenon. Few measures examine care setting transitions in palliative care, yet domains identified in other populations are likely relevant for patients receiving palliative care. AIM: Gain insight into how patients experience three domains, discharge readiness, transition quality, and discharge-coping, during hospital-to-home transitions. DESIGN: Longitudinal, convergent parallel mixed methods study design with two data collection visits: in-hospital before and 3-4 weeks after discharge. Participants completed scales assessing discharge readiness, transition quality, and post discharge-coping. A qualitative interview was conducted at both visits. Data were analyzed separately and integrated using a merged transformative methodology, allowing us to compare and contrast the data. SETTING AND PARTICIPANTS: = 14) were eligible if they received a palliative care consultation and transitioned to home-based palliative care. RESULTS: Results were organized aligning with the scales; finding low discharge readiness (5.8; IQR: 1.9), moderate transition quality (66.7; IQR: 33.33), and poor discharge-coping (5.0; IQR: 2.6), respectively. Positive transitions involved feeling well supported, managing medications, feeling well, and having healthcare needs met. Challenges in transitions were feeling unwell, confusion over medications, unclear healthcare responsibilities, and emotional distress. CONCLUSIONS: We identified aspects of these three domains that may be targeted to improve transitions through intervention development. Identified discrepancies between the data types should be considered for future research exploration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".