The Infant Cuddler Study: Evaluating the effectiveness of volunteer cuddling in infants with neonatal abstinence syndrome
Bibliographic record
Abstract
OBJECTIVES: St. Michael's Hospital launched a volunteer cuddling program for all infants admitted into the neonatal intensive care unit in October 2015. The program utilizes trained volunteers to cuddle infants when caregivers are not available. This was a pilot study to assess the impact of a volunteer cuddle program on length of stay (LOS) and feasibility of implementation of the program. METHODS: A mixed methods approach was utilized to measure both quantitative and qualitative impact. A pilot cohort study with a retrospective control group assessed the feasibility of implementing a volunteer cuddling program for infants with neonatal abstinence syndrome (NAS). Length of stay was used as a surrogate marker to measure the impact of cuddling on infants being treated for Neonatal Abstinence Syndrome. Focus groups using semi-structured interviews were conducted with volunteers and nurses at the end of the pilot study. RESULTS: LOS was reduced by 6.36 days (U=34, P=0.072) for infants with NAS in the volunteer cuddling program. Focus groups with both bedside nurses and program volunteers described a positive impact of cuddling programs on infants, families, staff, and volunteers alike. CONCLUSIONS: The study results suggest that the volunteer cuddling program may reduce LOS in infants with NAS and have potential economic savings on hospital resources. However, larger prospective cohort studies are needed to confirm these results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".