Qualitative Exploration of Health Professionals’ Perceptions of Addressing Malnutrition Within the First 1,000 Days
Bibliographic record
Abstract
OBJECTIVE: Explore health professionals' perceptions toward how to address malnutrition within the first 1,000 days of life in underresourced communities. DESIGN: A qualitative explorative-descriptive study using 8 face-to-face focus group discussions. SETTING: Health facilities serving underresourced communities within Nelson Mandela Bay, Eastern Cape Province, South Africa. PARTICIPANTS: Fifty-six health professionals (n = 13 doctors, n = 28 nurses, n = 6 dietitians, and n = 9 social workers) aged between 20 and 60 years, with 1-16 years (5 years average) of working experience. The majority (n = 53; 94.6%) were women. PHENOMENON OF INTEREST: Health professionals' perceptions of effective methods or strategies to address malnutrition are referred to as undernutrition. ANALYSIS: Content analysis. RESULTS: Health professionals perceived socioeconomic conditions; caregiver lack of nutrition knowledge; and behavioral, cultural, and generational infant feeding practices as contributing factors to malnutrition. Participants recommended efforts to strengthen the availability, accessibility, and utilization of contraception, especially for teenagers, increase support to caretakers of children from families, health facilities, and communities, and a multisector and multidisciplinary approach to improve social determinants of health in underresourced communities. CONCLUSIONS AND IMPLICATIONS: To address malnutrition within the first 1,000 days of life, data supports that health professionals in underresourced communities require a multisector, multidisciplinary approach. This approach entails educational interventions, peer mentoring and community empowerment through support to and involvement of caregivers of children.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".