The Maternal Support Framework Studying Mothers’ Perceived Understanding and Support During Excessive Infant Crying: Exploratory Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Excessive infant crying affects approximately 20% of families and can lead to parental distress, anxiety, and strained relationships. Despite its prevalence, many parents report feeling misunderstood and unsupported during these challenging periods. OBJECTIVE: This study aimed to gain in-depth insight into mothers' perceptions of being understood and supported in the context of excessive infant crying, focusing on three key stakeholder groups: partners, the personal network, and health care professionals. Based on these results, the study sought to develop a maternal support framework regarding excessive crying that could guide future research and practice, as well as support strategies. METHODS: Using a qualitative approach supplemented by quantitative measures, through an online survey, three open-ended questions were included on how mothers would like to be understood and supported by the three stakeholder groups (ideal situation) and six 6-point Likert scales on mothers' current perceived understanding and support regarding the three stakeholder groups (current situation). Descriptive statistics were used to examine current levels of understanding and support, and an inductive thematic analysis was applied to identify the ideal key support elements. RESULTS: Data were collected from 432 mothers (n=238, 55.1% Dutch; n=194, 44.9% Flemish; mean age 33 years, range 21-45 years). Regarding the current situation, mothers rated health care professionals lowest in perceived understanding and support, with 50.6% (n=219) feeling little or no understanding and 47.1% (n=203) reporting little or no support. Similar patterns were found in the personal network (n=184, 42.6%, and n=164, 38%, respectively). Partners were perceived as most supportive, with only 17.6% (n=76) of mothers reporting little or no understanding and 21.8% (n=94) reporting little or no support. Based on the thematic analysis of the qualitative data, the ideal situation was framed in the newly developed maternal support framework. This framework identifies 25 distinct support forms, of which 12 (48%) are common support forms (partner, personal network, health care professionals, eg, listen actively), 6 (24%) are related to partners (eg, be physically present), 6 (24%) concern the support of health care professionals (eg, refer appropriately), and 1 (4%) is specific to the personal network (cope with the crying). CONCLUSIONS: This study underscores the need for holistic, family-centered approaches to supporting families with excessively crying infants. The proposed maternal support framework offers a foundation for developing tailored interventions that reflect the diverse roles of partners, personal networks, and health care professionals in maternal well-being.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".