Task-shifting for cardiovascular risk factor management: lessons from the Global Alliance for Chronic Diseases
Bibliographic record
Abstract
Task-shifting to non-physician health workers (NPHWs) has been an effective model for managing infectious diseases and improving maternal and child health. There is inadequate evidence to show the effectiveness of NPHWs to manage cardiovascular diseases (CVDs). In 2012, the Global Alliance for Chronic Diseases funded eight studies which focused on task-shifting to NPHWs for the management of hypertension. We report the lessons learnt from the field. From each of the studies, we obtained information on the types of tasks shifted, the professional level from which the task was shifted, the training provided and the challenges faced. Additionally, we collected more granular data on 'lessons learnt ' throughout the implementation process and 'design to implementation' changes that emerged in each project. The tasks shifted to NPHWs included screening of individuals, referral to physicians for diagnosis and management, patient education for lifestyle improvement, follow-up and reminders for medication adherence and appointments. In four studies, tasks were shifted from physicians to NPHWs and in four studies tasks were shared between two different levels of NPHWs. Training programmes ranged between 3 and 7 days with regular refresher training. Two studies used clinical decision support tools and mobile health components. Challenges faced included system level barriers such as inability to prescribe medicines, varying skill sets of NPHWs, high workload and staff turnover. With the acute shortage of the health workforce in low-income and middle-income countries (LMICs), achieving better health outcomes for the prevention and control of CVD is a major challenge. Task-shifting or sharing provides a practical model for the management of CVD in LMICs.
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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 | 0.057 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".