Finding Fallers Using Telephone Versus Home Interviews and Laypersons Versus Health Professionals: Is the Information Valid?
Bibliographic record
Abstract
Objective and Design: This study determined the consistency of falls reporting between a telephone and in-person home interview using a cross-sectional design. Response consistency between the modes according to type of telephone interviewer, layperson, or health professional was also examined. Subjects: Three-hundred sixty-six community-dwelling individuals who were at high risk for falls because of a previous stroke or hip fracture participated: 107 required proxy assistance during interviewing. Results: The telephone and home interview provided similar estimates of the percentages of individuals not falling in the past month, 90% versus 92%. The overall sensitivity of the telephone interview to identifying fallers was 97% with a corresponding specificity of 98%. McNemar's X2 statistic indicated a significant difference (X2 = 5.44; p < .05) between the modes on the reporting of falls such that 38 falls were reported during the telephone interview versus 31 during the home interview. Layperson telephone interviewers were able to detect fallers as successfully as health professionals. Conclusion: The findings suggest that a telephone interview performed by a trained layperson may offer a cost-reduced means of identifying community-dwelling individuals at high risk of falls. Given the strong evidence that falls prevention programs are highly successful, this cost-effective strategy to detect individuals at high risk for falls is promising for injury prevention and health promotion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".