A Concept Analysis of Maternal Resilience against Pregnancy-Related Mental Health Challenges in Low- and Middle-Income Countries
Bibliographic record
Abstract
Suicide accounts for 33% of deaths of women during the postnatal period in many low- and middle-income countries (LMICs). Resilience refers to an ability to adapt and recover from adversity or misfortune. Resilience building against mental health challenges during pregnancy and the postnatal period is critical for women to raise their child efficiently and maintain a healthy life. The exploration of maternal resilience against mental health challenges including its developmental processes and the determinants of its successful or unsuccessful cultivation among mothers during pregnancy and childbirth is of paramount importance. Understanding why a subset of mothers effectively develops resilience while others significantly struggle is critical for devising targeted interventions and support mechanisms aimed at improving maternal well-being. This inquiry not only seeks to delineate the factors that contribute to or hinder the development of resilience but also aims to inform the creation of comprehensive support systems that can bolster maternal health outcomes. This paper endeavors to present a comprehensive analysis of maternal resilience, aiming to cultivate a nuanced and profound understanding of the concept within the framework of previous traumatic events and adverse pregnancy outcomes in LMICs. The eight-step method approach proposed by Walker and Avant was utilized for this concept analysis. Several defining attributes were identified in the analysis including social adaptation, support system, optimistic approach, and mindfulness. This analysis contributes to knowledge advancement regarding maternal resilience and provides nurses and other healthcare professionals with a clear understanding of the concept of maternal resilience to help promote resilience among mothers.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".