The Value of Patient Narratives in the Assessment of Older Patients Presenting with Falls
Bibliographic record
Abstract
BACKGROUND PURPOSE: Falls are a common and serious health problem experienced by older persons. The perception and interpretation of the fall experience can influence the long-term consequences of the event. In this pilot study, we explored whether there would be additional value in obtaining a patient narrative as part of the assessment of an older person who had fallen. METHODS: We conducted narrative interviews on a convenience sample of five older patients referred to the Calgary Fall Prevention Clinic (CFPC). Phenomena from the narratives were generated using original audio recordings. A focus group of four CFPC health professionals discussed similarities and differences between the narratives and the CFPC assessments conducted on these subjects without access to the narratives. RESULTS: Patient narratives revealed additional information about the person's emotional response to their falls and overall health status, their strengths that could be utilized in implementing a care plan, and what they had done personally to prevent further falls. CONCLUSIONS: Including patient narratives within standard fall-risk assessments could aid in understanding the emotional impact of falls on older patients and how they might respond to interventions. A challenge would be incorporating this within the time restraints of routine clinical practice.
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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 | 0.022 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".