Understanding The Workforce That Supports Māori And Pacific Peoples With Type 2 Diabetes To Achieve Better Health Outcomes
Bibliographic record
Abstract
Abstract Background: Prevalence of Type 2 diabetes mellitus (T2DM) is high among Māori and other Pacific Island peoples in New Zealand. Current health services to address T2DM largely take place in primary healthcare settings and have, overall, failed to address the significant health inequities among Māori and Pacific people with T2DM. Culturally comprehensive T2DM management programmes, aimed at addressing inequities in Māori or Pacific diabetes management and workforce development, are not extensively available in New Zealand. Deliberate strategies to improve cultural safety, such as educating health professionals and fostering culturally safe practices must be priority when funding health services that deliver T2DM prevention programmes. There is a significant workforce of community-based, non-clinical workers in South Auckland delivering diabetes self-management education to Māori and Pacific peoples. These include dietitians, community health workers and, more recently, kai manaaki (KM), but there is little information about these workers and their perspectives, challenges, effectiveness, and success in delivering these services. This study aimed to understand perspectives and characteristics of KM and other community-based, non-clinical health workers, with a focus on how they supported Māori and Pacific Peoples living with T2DM to achieve better outcomes.Methods: This qualitative study was undertaken underpinned by the Tangata Hourua research framework. Focus groups with dietitians, community health workers (CHWs) and KM took place in South Auckland, New Zealand. Thematic analysis of the transcripts was used to identify important key themes. Results: Analysis of focus group meetings identified three themes common across the groups: whakawhanaungatanga (actively building relationships), cultural safety, and cultural alignment. However, there appeared to be two key differences for KM and CHWs, who both preferred a multidisciplinary approach and described their experiences of feeling un/valued in their roles, when compared with dietitians. Generally, all three groups agreed that their roles required good relationships with the people they were working with and an understanding of the contexts in which Māori and Pacific Peoples with T2DM lived. Conclusions: Supporting community based, non-clinical workers to build meaningful and culturally safe relationships with Māori and Pacific people has potential to improve diabetes outcomes.
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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 | 0.006 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".