Caring for breastfeeding mothers in disaster relief camps: A call to innovation in nursing curriculum
Bibliographic record
Abstract
During natural disasters, people are forced to flee their homes and resettle in temporary disaster relief camps (such as huts, tents, and transitional shelters) that are often located on barren ground, far from cities. Disaster relief camps are one of the most vulnerable settings where women are at risk of discontinuing their breastfeeding practices. A critical ethnographic study undertaken with the internally displaced mothers residing in disaster relief camps in Pakistan revealed that the availability of formal support from healthcare professionals is one of the key determinants that shape the breastfeeding experiences of the displaced mothers. Hence suggested the need for innovative strategies in the nursing curriculum to build the capacity of nurses to provide culturally sensitive care to breastfeeding mothers affected by disaster and displacement. Considering these findings, it is recommended that nursing educational settings must include courses on "caring for the vulnerable population during a disaster" at the baccalaureate, graduate, and post-graduate levels. The inclusion of these courses will foster nurses to understand the needs of the displaced community, identify the importance of making a difference through collaborative work, and take part in designing innovative interventions (surrounding health, housing, economic upliftment, and well-being) for the displaced communities. Moreover, onsite clinical experience in disaster relief camps is recommended. This will enhance nurses' competence, hands-on skills, knowledge, and cultural sensitivity while providing care to displaced mothers with a variety of clinical presentations and breastfeeding concerns. Continuing education sessions and seminars must be organized for nurses to update their knowledge about breastfeeding and facilitate evidence-based practice in the setting of disaster relief camps.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".