Understanding the context of hospital transfers and away-from-home hospitalisations for Māori
Bibliographic record
Abstract
In Aotearoa New Zealand, people regularly travel away from their home to receive hospital care. While the role of whānau support for patients in hospital is critical for Māori, there is little information about away-from-home hospitalisations. This paper describes the frequency and patterning of away-from-home hospitalisations and inter-hospital transfers for Māori. Data from the National Minimum Dataset (NMDS), for the 6-year period of 1 January 2009-31 December 2014, were analysed. Basic frequencies, means and descriptive statistics were produced using SAS software. We found that more than 10% of all routine hospitalisations constituted an away-from-home hospitalisation for Māori; that is, a hospitalisation that was in a district health board (DHB) other than the DHB of usual residence for the patient. One quarter (25.19%) of transfer hospitalisations were to a DHB other than the patient's DHB of domicile. Away-from-home hospital admissions increase for Māori as deprivation increases for both routine and transfer admissions, with over half of Māori hospital admissions among people who live in areas of high deprivation. This analysis aids in understanding away-from-home hospitalisations for Māori whānau, the characteristics associated with these types of hospitalisations and supports the development and implementation of policies which better meet whānau Māori needs. The cumulative impact of the need to travel to hospital for care, levels of poverty and a primarily reimbursement-based travel assistance system all perpetuate an unequal cost burden placed upon Māori whānau.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".