Auxiliary Nurse Midwife: What Determines Her Place of Residence?
Bibliographic record
Abstract
Large numbers of health workers in rural areas of developing countries are not available to the communities they are supposed to serve. In India large numbers of female extension health workers (auxiliary nurse midwives or ANMs) do not reside within their work area limiting the communities’ access to health services. This study was conducted to understand the factors that an ANM considers in deciding where to reside and the interplay of these factors in influencing the decision. The study was conducted in a district of southern Rajasthan India. Secondary data was reviewed to identify the availability of basic amenities and infrastructure—this was supplemented with self-administered questionnaires; and in-depth interviews on a sub-sample. Analysis revealed that age of the ANM <30 or more than 45 years presence of a middle or high school within the sub-center area and distance of the place of posting being more than 20 kilometers from the city were significantly associated with residing within the sub-center area. Non-availability of education facilities for children and perceptions of insecurity seem to be major factors that deter ANMs from staying in a village. Supervisors or the community neither assist them in facing these difficulties nor enforce accountability for not residing. Recent devolution of administrative control of ANMs to local self-governments could be helpful in ensuring that they reside within the work area provided these local bodies are also entrusted with responsibility to ensure their safety and comfort. Formation of a committee at the district level with suitable representation of the civil society will provide the ANMs a platform to air and redress their problems related to living and working. There is also an urgent need to have an explicit human resource policy for the health sector that addresses issues such as recruitment posting career path supervision and accountability of health workers. (authors)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".