Callers' Ability to Understand Advice Received from a Telephone Health‐Line Service: Comparison of Self‐Reported and Registered Data
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Bibliographic record
Abstract
OBJECTIVE: To validate users' perception of nurses' recommendations to look for another health resource among clients seeking teleadvice. To analyze the effects of different users' and call characteristics on the incorrectness of the self-report. DATA SOURCES/STUDY SETTING: This study is a secondary analysis of data obtained from 4,696 randomly selected participants in a survey conducted in 1997 among users of Info-Santé CLSC, a no-charge telenursing health-line service (THLS) available all over the province of Québec. STUDY DESIGN/DATA COLLECTION: Self-reported advice from follow-up survey phone interviews, conducted within 48 to 120 hours after the participant's call were compared to the data consigned by the nurse in the computerized call record. Covariables concerned characteristics of callers, context of the calls, and satisfaction about the nurses' intervention. Association between these variables and inaccurate reports was identified using multinomial logistic regression analyses. PRINCIPAL FINDINGS: Advice to consult were recorded by the nurse in 42 percent of cases, whereas 39 percent of callers stated they had received one. Overall disagreement between the two sources is 27 percent (12 percent by false positive and 15 percent by false negative) and kappa is 0.45. Characteristics such as living alone (adjusted OR = 2.5), calls relating to psychological problems (OR = 2.8), perceived seriousness (OR = approximately 2.6), as well as others, were associated with inaccurate reports. CONCLUSIONS: Telephone health-line providers should be aware that many callers appear to interpret advice to seek additional health care differently than intended. Our findings suggest the need for continuing quality control interventions to reduce miscommunication, insure better understanding of advice by callers, and contribute to more effective service.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it