The STEP Program—A Qualitative Study of the Supportive Therapeutic Excursion Program and Its Effect on Enabling Parental Self-Efficacy and Connectedness after the Stress-Experience of the NICU
Bibliographic record
Abstract
As a child is born, so are the parents with new roles assumed. Attachment is secured, and parents quickly become the experts in their child. With preterm birth, however, this quiet and essential period of discovery is interrupted. It is described as a traumatic and stressful experience. Programs that aim to address this focus on education and support. This qualitative study explores the effect on parents and staff of a novel and simple intervention of parents walking their baby in a stroller (Supportive Therapeutic Excursion Program, STEP). This study used a qualitative methodology that included 20 semi-structured interviews for content saturation to explore the experiences of parents and staff participating in the STEP program. We found an overall positive experience reported by both parents and nursing staff with STEP. Analysis revealed that as autonomy is fostered with parent education around transition to home, STEP brought a normalcy that was reported to bolster participating parents' self-efficacy and connectedness, two important elements for recovery from stressful situations. The potential impact of STEP can be far reaching because, although future research is needed, these data suggest that inexpensive programs that promote normalcy may enable self-efficacy and connectedness prior to hospital discharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".