Dementia informal caregivers’ experience with hospital discharge planning post-discharge
Bibliographic record
Abstract
Little is known about dementia informal caregivers’ experience with discharge planning, \nand whether the discharge planning hospital team, including the physician, nurse \npractitioner (NP), social worker (SW), and other allied health practitioners, meet the \nneeds of caregivers both in- and post-hospital. This interpretative phenomenological study \nexamines dementia informal caregivers’ experience with hospital discharge planning \npost-discharge from an Ontario urban hospital. Five informal caregivers were interviewed \nusing semi-structured telephone interviews, and data was analyzed using Benner’s \ninterpretative analysis process (1985; 1994). Research findings suggest that dementia \ninformal caregivers consider their role as challenging due to many stressors and demands \nand poor discharge planning. Informal caregivers reveal that information sharing and the \narrangement of community resources during the discharge planning process was \ninconsistent. They felt abandoned and unsupported throughout the discharge plan, as \ndefined by lack of communication and dissemination of caregiver resources. As a result, \ncaregivers had difficulty understanding and managing their loved ones’ health care needs \nafter hospital discharge. Research findings can help inform discharge planning practices \nand standards as it pertains to dementia informal caregivers.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".