Implementation of a Hearing Loss Screening Intervention in Primary Care
Bibliographic record
Abstract
PURPOSE: Hearing loss (HL) is underdiagnosed and often unaddressed. A recent study of screening for HL using an electronic prompt showed efficacy in increasing appropriate referrals for subsequent testing. We build on the results of this study using a qualitative lens to explore implementation processes through the perspectives of family medicine clinicians. METHODS: We collected clinic observations and semistructured interviews of family medicine clinicians and residents who interacted with the HL prompt. All data were analyzed using thematic, framework, and mixed methods integration strategies. RESULTS: We interviewed 27 clinicians and conducted 10 observations. Thematic analysis resulted in 6 themes: (1) the prompt was overwhelmingly viewed as easy, simple to use, accurate; (2) clinicians considered prompt as an effective way to increase awareness and conversations with patients about HL; (3) clinician and staff buy-in played a vital role in implementation; (4) clinicians prioritized prompt during annual visits; (5) medical assistant involvement in prompt workflow varied by health system, clinic, and clinician; (6) prompt resulted in more conversations about HL, but uncertain impact on patient outcomes. Themes are presented alongside constructs of normalization process theory and intervention outcomes. CONCLUSION: Integration of a HL screening prompt into clinical practice varied by clinician buy-in and beliefs about the impact on patient outcomes, involvement of medical assistants, and prioritization during clinical visits. Further research is needed to understand how to leverage clinician and staff buy-in and whether implementation of a new clinical prompt has sustained impact on HL screening and patient outcomes.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".