A scoping review of parental experiences caring for their hospitalised medically fragile infants
Bibliographic record
Abstract
AIM: To synthesise and summarise evidence from published research articles regarding parental experiences caring for their hospitalised medically fragile infant. METHODS: We searched four electronic databases in April 2018 using three main concepts individually and in combination: infant, medically fragile, parents. We examined articles about experiences of parents caring for the medically fragile infant in a hospital setting. We conducted thematic analysis on the 34 included articles. RESULTS: Parents experienced high rates of depressive symptoms, depression, stress, anxiety, distress and post traumatic stress. Parent-infant interactions were disrupted. Parents experienced loss and worry in response to the diagnosis of their infant, which altered or delayed parental role attainment. Supports and coping were key for parents to manage their stress. CONCLUSION: Parents of medically fragile infants experience multiple stressors, elevated levels of mental health difficulties, trouble attaining their parental role and often struggle to cope. Development of interventional research is needed to test targeted strategies aimed at reducing parental stress and mental health difficulties. Interventions should include: screening for parental mental health, psychological support, healthcare professional education, strategies to enhance parent-infant interactions and improved relationship competencies among healthcare professionals.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".