Self management pilot study on women with breast cancer: lessons learnt in Malaysia.
Bibliographic record
Abstract
OBJECTIVE: With increasing survival rates, breast cancer is now considered a chronic condition necessitating innovative care to meet the long-term needs of survivors. This paper presents the findings of a pilot study on self-management for women diagnosed with breast cancer and their implications for Asian health care providers. METHODS: A pre-test/ post-test pilot study was conducted to gain preliminary insights into program feasibility and barriers to participation, and to provide justification for a larger trial. RESULTS: The study found the 4 week self-management program feasible and acceptable, with a favourable trend in quality of life. The recruitment barriers ranged from competing medical appointments, uncollaborative health providers, linguistic barriers and social-household concerns. Supporting facilitators identified were family, health professionals and fellow participants ("buddies"). Lessons from the study are discussed with regard to Asian health providers. CONCLUSION: There is preliminary evidence that self management is a workable and potentially useful model even in an Asians entrenched-hierarchical medical model of care. The initial challenge was breaking down barriers in acceptancee of a collaborative stance. A clinical trial is now warranted to gather more evidence.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".