Family Caregivers' Experiences with Stroke Survivors' Weekend Home Passes from In-patient Rehabiltation
Bibliographic record
Abstract
This qualitative study investigated the experiences of family caregivers who cared for a stroke survivor throughout weekend passes from an in-patient rehabilitation facility. Following a Husserlian phenomenological perspective, I interviewed 15 family caregivers of stroke survivors on two separate occasions: within one week after their first scheduled weekend pass, and again two weeks after their final transition home. Interviews followed a semi-structured interview guide, and were audio recorded. Interviews were transcribed verbatim, and analyzed using the principles outlined by Giorgi (1997). Analysis of the caregivers’ descriptions of their experiences led to five themes: (i) Caregivers feel overwhelmed throughout the weekend pass, (ii) Access to supportive resources influences caregiving experiences, (iii) Caregivers adjust to the weekend pass throughout its subsequent use, (iv) Caregivers feel a responsibility and a need to be included by health professionals in the care of their family stroke survivor, and (v) Caregivers describe the weekend pass as a means towards recovery. This study has implications for future health services. By appropriately training and supporting caregivers throughout the weekend pass health care professionals can increase the positive experiences of caregivers, and optimize the opportunity to prepare caregivers and stroke survivors for the eventual transition home.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".