Listening with a narrative ear: Insights from a study of fall stories in older adults.
Bibliographic record
Abstract
OBJECTIVE: To determine the value of adding a patient narrative to the clinical assessment of falls in the elderly. DESIGN: Qualitative study of interviews. SETTING: A fall prevention clinic in Calgary, Alta. PARTICIPANTS: Fifteen older adults on a wait list for assessment by the fall clinic and the physiotherapists who assessed them. METHODS: Participants' stories were audiorecorded and later transcribed and summarized. Stories were collected using open-ended questions, first inviting participants to tell the interviewer about themselves, and then the circumstances of their falls and their reflections on them. In a subsequent visit, transcriptions or summaries were returned to patients for member checking. Narratives were read and analyzed by all 4 investigators using a narrative approach and a close-reading technique. With the patients' additional consent, stories were shared with the fall prevention team for their insights and reactions. Interviews with physiotherapists were audiorecorded and transcribed. MAIN FINDINGS: The narrative analysis provided new insights into the attitudes about and perceptions of the causes of falls, their effects, and rehabilitation. Close reading exposed presentation of self, locus of control, and underlying social and emotional issues. CONCLUSION: The addition of patient narratives to clinical assessments offers clinicians an understanding of patients' perspectives, which can be used to better engage patients in rehabilitation.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".