The inclusion of mothers in human resources for health planning
Bibliographic record
Abstract
AIM: This paper examines the possibility of including families, particularly mothers, within the health workforce using the human resource for health planning model to improve newborn outcomes. BACKGROUND: In many low- and middle-income countries, there is a critical shortage of healthcare providers which impacts care for the neonatal population. A maternal and newborn health need that is prevalent in such countries is the care available between pregnancy and the postnatal period, where significant maternal and newborn deaths occur. SOURCES OF EVIDENCE: Using the population health need of the neonatal population in Tanzania, this paper explores the opportunity to include mothers as an additional human resource for health within the Needs-Based Health Human Resources and Health Systems Planning model. DISCUSSION: In relation to educating and engaging family caregivers, the possible extension of the health workforce to include mothers as a response to meeting the healthcare needs of the neonatal population has yet to be explored. Through mothers and healthcare providers working together to address the population health need of essential newborn care, it offers a way forward for planning the resources needed in a health system. If utilized, mothers offer the opportunity to supplement the demand for human resources for health in the provision of newborn care, without replacing healthcare providers. CONCLUSION: Mothers as potential members of the health workforce furthers the health system as a whole whereby population health needs are addressed and newborn mortality declines. IMPLICATIONS FOR HEALTH POLICY: To solve the critical gap based on the supply of and demand for providers including doctors, nurses and midwives, a broader look at innovative solutions is essential. IMPLICATIONS FOR NURSING PRACTICE: Mothers offer the opportunity to supplement the available human resources for health in the provision of newborn care, thus helping to close existing gaps.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".