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Record W4385651930 · doi:10.29390/001c.84446

Skin-to-skin therapy on high-frequency jet ventilation: A trauma-informed best practice

2023· article· en· W4385651930 on OpenAlexaffvenue
Dallyce Varty, Kuljit Minhas, Sarah Gillis, Sarah Rourke

Bibliographic record

VenueCanadian Journal of Respiratory Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsFraser HealthRoyal Columbian Hospital
Fundersnot available
KeywordsMedicineVentilation (architecture)Psychological interventionEmergency medicineMedical emergencyIntensive care medicineNursingEngineering

Abstract

fetched live from OpenAlex

Objective: To mitigate trauma for infants on high-frequency jet ventilation by decreasing exposure to noise and facilitating skin-to-skin therapy. Design: Key drivers were identified, and we designed and implemented equipment and processes through a series of interventions. A mixed methods evaluation was used. Retrospective chart reviews assessed safety (unplanned extubation) and stability parameters. Semi-structured interviews were conducted to understand parent and staff experiences. Results: Stability parameters demonstrated safe skin-to-skin therapy. Data from the interviews showed that parents and staff experiences focused on safety, connection and healing. Conclusion: Implementing safe processes to support skin-to-skin therapy during high-frequency jet ventilation is possible. We hope other units will be encouraged to examine their current practices for infants on high-frequency jet ventilation to help mitigate trauma for infants and parents while enhancing staff satisfaction.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.046
GPT teacher head0.314
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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