MétaCan
Menu
Back to cohort
Record W2767639019 · doi:10.28984/drhj.v1i0.10

Hydrotherapy as a Nursing Intervention for Labour Pain

2017· article· en· W2767639019 on OpenAlexaffvenue
Erin Cowan, Roberta Heale, Judith Horrigan, Irene Koren

Bibliographic record

VenueDiversity of Research in Health Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsLaurentian University
Fundersnot available
KeywordsHydrotherapyCINAHLMedicineNursingGuidelineMEDLINEIntervention (counseling)Physical therapyInclusion (mineral)Alternative medicinePsychologyPsychological intervention

Abstract

fetched live from OpenAlex

This literature review provides a brief history of hydrotherapy during labour, a summary of the existing literature, and implications for practice. The objective of the literature review is to explore the evidence regarding the safety and efficacy of hydrotherapy as a method to alleviate labour pain during the first stage of labour. A comprehensive review of the literature was conducted in August 2016 using CINAHL, ProQuest, and MedLine databases. Keywords used were: hydrotherapy, water immersion, intrapartum, labour and/or nurse with truncation and Boolean methods. The inclusion criteria for the search were: articles available in English, accessible electronically, peer-reviewed, and with year restrictions of 2005-2016. The search yielded one practice guideline, four recommendations from regulatory colleges, three systematic reviews, and five single studies. The author concludes that hydrotherapy for low risk parturients is a safe and effective method of pain control that empowers nurses and promotes positive patient outcomes.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.238
GPT teacher head0.552
Teacher spread0.314 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueDiversity of Research in Health JournalSame topicPregnancy-related medical researchFrench-language works237,207