Innovative family history application-Provider’s perspectives
Bibliographic record
Abstract
Context: A complete and up-to-date family history (FH) is imperative in primary care (PC). The identification of high-risk individuals may enable appropriate follow-up including genetic testing, personalized screening and management. Complete FH is rarely documented in the electronic medical record (EMR). Objective: To explore family physicians’ (FP) experiences and perceptions of an innovative EMR-integrated strategy to collect FH. Study design and analysis: Qualitative study involving telephone interviews with FPs. Thematic analysis was used for identifying, analyzing and reporting patterns. Three researchers independently performed line-by-line open coding of interview transcripts then met to discuss codes. An iterative process was used, meeting frequently to modify the interview and coding guide as new themes emerged. A coding framework was used to analyze the remaining transcripts. Major themes were identified until saturation was reached. An inductive approach to data analysis using the constant comparative method was used. Setting: Randomly selected PC team practices affiliated with University of Toronto Practice-Based Research Network in Ontario, Canada. Population studied: Eligible FPs from 3 intervention sites. Intervention: Emailed patient invitation to complete validated FH questionnaire, automatic EMR upload, FP notification and links to clinical support tools. Outcome measures: FPs’ experiences and perceptions of the strategy. Results: 15/20 FPs were interviewed. Average age was 48y, 71% identified as female, 43% practiced for less than 10 years and there was a range in practice type; community (7%), academic (57%), combined (36%). Six major themes were identified: FH provides important information about hereditary risk, permitting tailored patient management; The intervention was a new way to opportunistically collect FH by leveraging technology; It facilitated meaningful discussions with patients, contributing to perceived good patient care; It increased awareness and knowledge regarding management; Comprehensive review disclosed new information which led to clinically relevant management changes; Strategies are needed to increase acceptability. Conclusion: FPs expressed the importance of routine FH collection and its implications for clinical management. Factors contributing to the intervention’s success included being patient-initiated and seamless EMR integration. The intervention needs tailoring to different contexts.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".