Supportive care needs of caregivers of individuals following stroke: a synopsis of research.
Bibliographic record
Abstract
Approximately 75% of stroke survivors are discharged from hospital to the community with varying degrees of residual neurological deficits (Heart & Stroke Foundation of Ontario, 2003). As a part of a masters' thesis, a systematic review was conducted to synthesize the research related to the identification of family needs during the acute phase of stroke in order to facilitate successful transition into the role of caregiver. Relevant articles were identified using: CINAHL, MEDLINE, All EBM Reviews, Psych Info, Embase, and AARP Ageline (1978 to December 2007). A Supportive Care Needs Framework (SCNF) (Fitch, 1994; 2008) was used to collect and analyze data. The utility of this framework was evaluated in capturing the spectrum of needs of the family caregivers of patients with stroke. Ten qualitative studies and seven quantitative studies were identified and analyzed by the author. The studies were equivocal in their reports of needs not being identified and addressed during hospitalization. The SCNF provided a comprehensive means of organizing the broad spectrum of needs of this population reported in the literature. No new domains were uncovered in the review.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".