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Record W2532694835 · doi:10.1097/hnp.0000000000000138

Mantram Repetition With Homeless Women

2016· article· en· W2532694835 on OpenAlexaff
Sally Weinrich, Jill E. Bormann, Dale Glaser, Sally Brosz Hardin, Mary K. Barger, Cabiria Lizarraga, Juan Del Rio, Carolyn B. Allard

Bibliographic record

VenueHolistic Nursing Practice · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsAllard Foundation
Fundersnot available
KeywordsRepetition (rhetorical device)PsychologyHealth carePhenomenology (philosophy)Qualitative researchPopulationMedicineClinical psychologyNursingGerontologySociology

Abstract

fetched live from OpenAlex

Women and families are the fastest growing segment of the homeless population. Negative attitudes of nurses toward homeless women are a major barrier to homeless women seeking health care. This cross-sectional, mixed-methods pilot study, conducted primarily by nurses, tested the Mantram Repetition Program for the first time with 29 homeless women. The Mantram Repetition Program is a spiritually based skills training that teaches mantram (sacred word) repetition as a cost-effective, personalized, portable, and focused strategy for reducing stress and improving well-being. For the cross-sectional, pretest-posttest design portion of the study, the hypothesis that at least half of the homeless women would repeat their mantram at least once a day was supported with 88% of the women repeating their mantram 1 week later. The qualitative portion of this study using phenomenology explored the women's thoughts on mantram week 2. Themes of mantram repetition, mantram benefits, and being cared for emerged. This groundbreaking, interventional, mixed-methods pilot study fills a gap in interventional homeless research.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.068
GPT teacher head0.454
Teacher spread0.386 · 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 designQualitative
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

Citations11
Published2016
Admission routes1
Has abstractyes

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