Enabling self-management support
Bibliographic record
Abstract
This guide identifies specific self-management support interventions which have demonstrated reach and impact, and identifies the factors that activate patients, clinicians and the healthcare system to engage with these interventions.The interventions presented in this guide reflect learning from working with approximately 9,000 patients in the Manaaki Hauora – Supporting Wellness campaign.The Manaaki Hauora – Supporting Wellness campaign:Counties Manukau has over 67,000 people with long-term conditions. More than half of these have diabetes, and more than quarter have two or more co-existing conditions.The Manaaki Hauora – Supporting Wellness campaign led by Ko Awatea, the centre for healthcare improvement and innovation at Counties Manukau Health, aimed to provide self-management support for people living with long-term conditions in Counties Manukau.The campaign covered 16 collaborative teams working in different settings and clinical contexts. Each team had a unique aim which contributed to the overall campaign aim. Ten of the teams, whose projects best illustrate the interventions with the greatest reach and impact, are featured in this guide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.038 |
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".