Peer support groups for families in Neonatology: Why and how to get started?
Bibliographic record
Abstract
AIM: To describe the development of peer-to-peer support meetings between parents of children in neonatal intensive care unit (NICU) and veteran resource parents who had a previous NICU experience. METHODS: The study had two steps: a needs assessment and a feasibility pilot study. Parental perspectives were investigated using mixed methods. RESULTS: One hundred and fifty-three parents were participated. NICU parents (89%) wished to meet resource parents to discuss: their parental role, normalising their experience and emotions, adapting to their new reality, control, guilt, trust and coping. Practical aspects of the meetings were tested/finalised. Resource parent moderators reported that the presence of more than one moderator per meeting was essential. A checklist of topics to discuss was developed. Having a diversity of moderators (fathers, diagnoses other than prematurity, for example) was judged important. The name of the meeting had an impact on attendance: there were less participants when the word "support" was used. The best location (central, parents' kitchen) and optimal time/duration of meetings, selection of parent moderators and compensation were also determined. CONCLUSION: Peer support meetings moderated by resource parents provide a unique and useful means to support NICU parents. Future investigations will explore whether these meetings will improve clinical outcomes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".