Improving chronic illness care - revisiting the role of care planning
Bibliographic record
Abstract
BACKGROUND: Chronic illness is increasingly being recognised as a widespread, debilitating and costly burden. Most models of care used in the acute care setting are inappropriate for chronic illness and are costly. OBJECTIVE: This article examines the goals of chronic illness care in the Australian general practice context and related issues from a conceptual perspective. It describes developments in care planning in Australia, particularly Medicare payments for care planning, and discusses how such developments can assist general practitioners in patient care. A case study of an Aboriginal patient with chronic illness is described to illustrate the issues discussed. DISCUSSION: Care planning/management based on a partnership model can bring about some success in management, even with the most difficult cases. Illness support, management of rest of life conditions and treatment and self management of disease are required. Care planning/management items, as part of the Enhanced Primary Care program provide incentive payments to address key models of improving complex chronic care. This can result in improved system organisation and self management of chronic illness.
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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| 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".