Enhancing interprofessional practice through the co-design of a holistic culturally and developmentally informed First Nations child health assessment
Bibliographic record
Abstract
BACKGROUND: This qualitative study explored staff experiences of co-designing and implementing a novel interprofessional (IP) First Nations child health assessment (the helpful check), developed in partnership with a remote North-Queensland Aboriginal CommunityControlled Health Organisation. METHOD: Eleven staff across two teams (family health and allied health) were involved in co-designing and implementing the child health assessment and associated IP practices. Interviews were undertaken using a semi-structured interview template and were audio recorded and transcribed verbatim. Data were analysed using thematic analysis. RESULTS: Three overarching themes were developed: (1) connect teams by building strong relationships; (2) leave space for helpful check processes to evolve; and (3) integrate helpful check processes into routine practice to sustain change. CONCLUSIONS: Results demonstrate how the incorporation of IP practices into a remote primary healthcare setting led to perceived benefits for both the health service staff and clients.
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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 | 0.032 | 0.030 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".