Stroke family caregivers’ support needs change across the care continuum: a qualitative study using the timing it right framework
Bibliographic record
Abstract
Purpose: Family caregivers provide essential support as stroke survivors’ return to community living, but it is not standard clinical practice to prepare or provide ongoing support for their care-giving role. In addition, health care professionals (HCPs) experiences with providing support to caregivers have not been explored previously. The objectives of this qualitative study were to: (1) explore the support needs over time from the perspective of caregivers, (2) explore the support needs over time from the perspective of HCPs, and (3) compare and contrast caregivers’ and HCPs’ perspectives. Methods: A qualitative study with stroke family caregivers (n = 24) and HCPs (n = 14). In-depth interviews were audio taped, transcribed, and analyzed using Framework Analysis. Results: Three main themes emerged concerning: (1) types and intensity of support needed; (2) who provides support and the method of providing support; and (3) primary focus of care. These themes are discussed in relation to the TIR framework. Conclusions: Caregivers’ needs for support and the individuals most suited to providing support change across the stroke survivor’s recovery trajectory. Changes to service delivery to better support caregivers may include: (1) addressing caregivers’ changing needs across the care continuum; (2) implementing a family-centered model of care; and (3) providing 7-day per week inpatient rehabilitation.Implications for RehabilitationCaregivers support needs change across the care continuumSupport programs should be offered outside usual working hoursHealth care professionals should address the needs of the stroke survivor and their family caregiverCaregivers benefit from receiving support from health care professionals, family, friends, and care-giving peers
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".