Patients’ Experiences With Vehicle Collision to Inform the Development of Clinical Practice Guidelines: A Narrative Inquiry
Bibliographic record
Abstract
OBJECTIVE: The purpose of this narrative inquiry was to explore the experiences of persons who were injured in traffic collisions and seek their recommendations for the development of clinical practice guideline (CPG) for the management of minor traffic injuries. METHODS: Patients receiving care for traffic injuries were recruited from 4 clinics in Ontario, Canada resulting in 11 adult participants (5 men, 6 women). Eight were injured while driving cars, 1 was injured on a motorcycle, 2 were pedestrians, and none caused the collision. Using narrative inquiry methodology, initial interviews were audiotaped, and follow-up interviews were held within 2 weeks to extend the story of experience created from the first interview. Narrative plotlines across the 11 stories were identified, and a composite story inclusive of all recommendations was developed by the authors. The research findings and composite narrative were used to inform the CPG Expert Panel in the development of new CPGs. RESULTS: Four recommended directions were identified from the narrative inquiry process and applied. First, terminology that caused stigma was a concern. This resulted in modified language ("injured persons") being adopted by the Expert Panel, and a new nomenclature categorizing layers of injury was identified. Second, participants valued being engaged as partners with health care practitioners. This resulted in inclusion of shared decision-making as a foundational recommendation connecting CPGs and care planning. Third, emotional distress was recognized as a factor in recovery. Therefore, the importance of early detection and the ongoing evaluation of risk factors for delayed recovery were included in all CPGs. Fourth, participants shared that they were unfamiliar with the health care system and insurance industry before their accident. Thus, repeatedly orienting injured persons to the system was advised. CONCLUSION: A narrative inquiry of 11 patients' experiences with traffic collision and their recommendations for clinical guidelines informed the Ontario Protocol for Traffic Injury Management Collaboration in the development of new Minor Injury Guidelines. The values and findings of the qualitative inquiry were interwoven into each clinical pathway and embedded within the final guideline report submitted to government.
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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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".