Perceptions and Practices of Parents in Caring for their Hospitalized Preterm Infants
Bibliographic record
Abstract
Hospitalized preterm infants are separated from parents in many countries, including Thailand, neonatal care has promoted parental involvement in caring for their infants to support breastfeeding and parent-infant bonding. This descriptive qualitative approach aimed to gain a better understanding of Thai parental involvement in caring for hospitalized preterm infants. Purposive sampling was used to select 22 parents, two grandmothers, and three nurses at a sick newborn unit of a regional hospital in Eastern Thailand. Data were collected through in-depth interviews, participant observation and clinical document reviews, from September 2014 to October 2015. The data were analyzed by using a thematic analysis. The findings revealed parents’ perceptions and caregiving practices regarding their involvement in caring for hospitalized preterm infants that could be categorized into five categories, 1) uncertainty about their child’s condition, 2) desire to be close to their preterm babies, 3) lack of confidence in providing care for their preterm babies, 4) overcoming difficulties in breastfeeding, and 5) socio-cultural factors influencing parental involvement. Parental involvement in caring for hospitalized preterm infants is crucial to the quality of infant care. The findings of this study could assist in evidence for developing a nursing intervention program to enhance and support parental involvement in caring for preterm infants.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".