Patients' Perceptions Regarding Home Telecare
Bibliographic record
Abstract
While home telecare's potential to reduce health care costs appears clear, patients' perceptions regarding this new technology have not been studied. We conducted structured interviews to elicit patients' perceptions regarding home telecare. We developed a 34-item survey instrument, which was administered during structured home interviews to a convenience sample of patients who were currently or had previously been enrolled in the Sonora Health System or University of California Davis home telecare pilot projects. Fifteen (56%) of the 27 past or present enrollees agreed to be interviewed. Most had either a neutral (9 of 15, 60%) or positive (5 of 15, 33%) outlook regarding home telecare before their enrollment. Following enrollment, all were either very satisfied (10 of 15, 67%) or somewhat satisfied (5 of 15, 33%) with services they had received. Fourteen of 15 (93%) were willing to receive home telecare services in the future, and all 15 would recommend home telecare to friends or family members. Despite education to the contrary, patients perceived that the presence of telecare equipment in the home implied 24-hour-a-day access to a nurse. Some interviewees felt uncomfortable disclosing intimate information during televisits, and others lamented the reduced amount of time nurses spent "socializing" as compared to in-person visits. Despite concerns regarding its confidentiality and its ability to approximate the social stimulation of in-person nursing visits, patients in these pilot trials seemed satisfied with home telecare and appeared ready to accept its widespread use.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".