Indigenous injury outcomes: life satisfaction among injured Maori in New Zealand three months after injury
Bibliographic record
Abstract
BACKGROUND: Māori, the indigenous population of New Zealand, experience numerous and consistent health disparities when compared to non-Māori. Injury is no exception, yet there is a paucity of published literature that examines outcomes following a wide variety of injury types and severities for this population. This paper aims to identify pre-injury and injury-related predictors of life satisfaction three months after injury for a group of injured Māori. METHODS: The Māori sample (n = 566) were all participants in the Prospective Outcomes of Injury Study (POIS). POIS is a longitudinal study of 2856 injured New Zealanders aged 18-64 years who were on an injury entitlement claims' register with New Zealand's no-fault compensation insurer. The well-known Te Whare Tapa Whā model of overall health and well-being was used to help inform the selection of post-injury life satisfaction predictor variables. Multivariable analyses were used to examine the relationships between potential predictors and life satisfaction. RESULTS: Of the 566 Māori participants, post-injury life satisfaction data was available for 563 (99%) participants. Of these, 71% reported satisfaction with life three months after injury (compared to 93% pre-injury). Those with a higher injury severity score, not satisfied with pre-injury social relationships or poor self-efficacy pre-injury were less likely to be satisfied with life three months after injury. CONCLUSIONS: The large majority of Māori participants reported being satisfied with life three months after injury; however, nearly a third did not. This suggests that further research investigating outcomes after injury for Māori, and predictors of these, is necessary. Results show that healthcare providers could perhaps put greater effort into working alongside injured Māori who have more severe injuries, report poor self-efficacy and were not satisfied with their pre-injury social relationships to ensure increased likelihood of satisfaction with life soon after injury.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".