An innovative model for integrated delivery of prevention, screening and palliative care services of cancers at health and wellness centres in Assam
Bibliographic record
Abstract
There is a consensus on delivering prevention, early detection and palliative care services as effective cancer control strategies in primary healthcare settings; however, examples of practical application are few. The study describes the implementation of integrated delivery of preventive, early detection and palliative care needs assessment through the frontline healthcare workers at the Health and Wellness Centre. The study employed a master trainer team of dentist and nurses trained in prevention and needs assessment of palliative care services who would further provide the handhold training to the Community Health Officers (CHO), multi purpose workers and Accredited Social Health Activist for awareness, prevention and generalist palliative care needs assessment. 2106 households with 256 people were screened as a result, with an average of around 30 screenings a day. Screen positivity rates was found to be 3.1% for the oral cancer, for breast cancer it was 1.8% while for cervical cancer it was 3.4%. While 0.5% households were identified in need of palliative care, all screened positive cases were provided counselling for further diagnostics and care at the cancer centre in the district. The ambulance services of 102 available in the state were arranged for people willing to undergo the diagnostics. The evidence generated has the potential for practical application with further testing and strengthening in the field.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".