Rural carers online: A feasibility study
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility (for a potential randomised controlled trial) of a computer intervention for improving social interaction and promoting the mental health of rural carers. DESIGN: The study combined pre- and post-intervention measures with interviews to determine the feasibility of the intervention and the acceptability of the study design to participants. The intervention consisted of providing 14 rural carers with computers and a 4-week training program on basic computer skills, using email and the Internet. SETTING: The study was conducted in a rural community setting. PARTICIPANTS: The carers were 12 women and two men, aged from 50 to 81 years, with an average of 65.5 years. MAIN OUTCOME MEASURES: Measures of social isolation (UCLA Loneliness Scale), depression (Geriatric Depression Scale), carer burden (Zarit Burden Interview) and computer confidence were taken at baseline and at a 3-month follow-up. Interviews were completed at follow-up to discuss outcomes of the study. A focus group discussion was conducted with 11 participants to discuss the study and resolve computer issues. RESULTS: Most carers reported increased confidence in email and Internet use. There was improvement for most participants in depressive symptoms and social isolation, but little change in carer burden. Participants identified many social benefits associated with the computer intervention, such as intergenerational connection, community building, skills and confidence and preparation for the future. CONCLUSION: The intervention was found to be practical and acceptable for a group of older carers. It was concluded that it would be feasible to conduct a large randomised controlled trial of the intervention.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".