Recruitment issues in healthcare research: the situation in home care
Bibliographic record
Abstract
A global shift in the setting of healthcare from hospitals and long-term care institutions to homes and communities has been accompanied by the growth of interest in the home as a site of healthcare research. Home care researchers have identified the recruitment of research subjects as a significant concern. The present descriptive, exploratory study used qualitative, semi-structured interviews with home care researchers (n = 9) to illuminate the challenges related to recruitment. The results suggest that while home care research shares recruitment issues common to other forms of health research, it has unique concerns. Factors affecting recruitment in home care studies include non-dedicated recruiters, the current context of healthcare restructuring, and gatekeeper and participant feelings about the home as a setting for care and research. Reasons for refusal to participate may be more complex in home care research given the meanings care recipients attribute to their 'homes'. Home care researchers may also face unique ethical and/or moral dilemmas. This paper recommends the routine reporting of recruitment problems, increased inclusion of minority subjects to ensure sample representativeness and further studies of the subjective meanings of 'home' as it is associated with healthcare treatment.
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 | 0.514 | 0.484 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.034 | 0.040 |
| Scholarly communication | 0.031 | 0.024 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".