Illness Narrative Master Plots Following Musculoskeletal Trauma and How They Change over Time, a Secondary Analysis of Data
Bibliographic record
Abstract
Introduction; to the best of the authors knowledge, no past research has established how illness narrative master plots are expressed initially and then if and how they change longitudinally following musculoskeletal trauma. The aim of the present research was to consider how specific master plots were expressed, interact, and change across time following musculoskeletal trauma. METHODS: A narrative analysis was undertaken that included individuals who had experienced a musculoskeletal traumatic injury. Individuals were included if they were an inpatient within 4 weeks of the first interview, had mental capacity to participate, and were able to communicate in English. Three interviews were undertaken (within 4 weeks of injury, then at 6- and 12-months post-injury). A 5-stage categorical form-type narrative analysis was performed. RESULTS: Twelve individuals (49.9 ± 17.5 years; 7 male, 5 female) completed interviews at three time points following the trauma event (<4 weeks, 6 months, and 12 months). Three main narrative master plots appeared to work together to facilitate a positive accommodation of the trauma event into the individual's life. These included the resumption narrative, the activity narrative, and the quest narrative. Finally, less often regressive narratives were identified, although these narratives were, at times, actively avoided. DISCUSSION: The current results provide important consideration for how narratives are used within clinical practice, in particular the value of how these three narratives could be accessed and promoted.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".