P.175 An exploration of the evolving perception of quality of life from the perspective of individuals living with a cervical spinal cord injury in Nova Scotia
Bibliographic record
Abstract
Background: Spinal cord injuries invoke enormous life changes for the individual, with impacts not just on physical functioning, but social and psychological well-being. Individuals learn to deal with these changes, and handle these new stressors in different ways. Extant literature suggest the majority of people eventually attain a quality of life (QoL) simular to able-bodied individuals. We sought to validate these observations in a contemporary cohort and specifically explore how patients’ perceptions evolve over time. Methods: We conducted hour-long semi-structured interviews with 15 individuals living with cervical spinal cord injuries. Interviews took place over the telephone or virtually via MS Teams. Interview transcripts were then analyzed using an iterative coding process and thematic analysis (NVivo). Results: The over-arching journey that most participants described was a continuous evolution in QoL, as they learned to adapt and function with their injury. However, these trajectories were disparate and heavily reliant on personal supports and resources available, their psychosocial enviornment and inherent coping strategies. Conclusions: This study emphasizes the unique nature of each person’s journey, and not all people attain a satisfactory QoL. Our approach needs to be individualized, adjusting to specific circumstances, in order to provide more inclusive and supportive care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".