Exploring Parental and Staff Perceptions of the Family-Integrated Care Model
Bibliographic record
Abstract
BACKGROUND: Family-integrated care (FICare) is an innovative model of care developed at Mount Sinai Hospital, Canada, to better integrate parents into the team caring for their infant in the neonatal intensive care unit (NICU). The effects of FICare on neonatal outcomes and parental anxiety were assessed in an international multicenter randomized trial. As an Australian regional level 3 NICU that was randomized to the intervention group, we aimed to explore parent and staff perceptions of the FICare program in our dual occupancy NICU. SUBJECTS AND DESIGN: This qualitative study took place in a level 3 NICU with 5 parent participants and 8 staff participants, using a post implementation review design. METHODS: Parents and staff perceptions of FICare were explored through focus group methodology. Thematic content analysis was done on focus group transcripts. RESULTS: Parents and staff perceived the FICare program to have had a positive impact on parental confidence and role attainment and thought that FICare improved parent-to-parent and parent-to-staff communication. Staff reported that nurses working with families in the program performed less hands-on care and spent more time educating and supporting parents. IMPLICATIONS FOR PRACTICE: FICare may change current NICU practice through integrating and accepting parents as active members of the infant's care team. In addition, nurse's roles may transition from bedside carer to care coordinator, educating and supporting parents during their journey through the NICU. IMPLICATIONS FOR RESEARCH: Further research is needed to assess the long-term impact of FICare on neonates, parents, and staff.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".