Does what we measure matter? Quality-of-life defined by adolescents with brain injury
Bibliographic record
Abstract
OBJECTIVES: To determine if domains included in popular measurement systems (e.g. the Peds QL™) reflect the adolescent survivor of a brain injury's sense of QoL and explore this relationship in reference to an emerging model of wellbeing in the adolescent with TBI. METHODS: Mixed methods; adolescent QoL assessed using the PedsQL™ self-report and a semi-structured interview created by the lead author. Adolescent self-report was compared to adolescent narratives. RESULTS: Ten adolescents participated. Adolescent PedsQL™ total was within normal limits. Adolescents reported that changes identified by the PedsQL were not important and did not impact on their sense of QoL. The importance on social components of QoL-as opposed to cognitive-provide additional support of the emerging model of wellbeing in adolescents with TBI. CONCLUSIONS: The PedsQL can identify changes post-TBI, but fails to consider whether these changes are relevant to the adolescent. Alternate methods of exploring QoL-which emphasize the interaction of social networks and friendships, should be considered to avoid an oblique view of QoL outcomes after TBI.
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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 | 0.025 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".