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Record W3048437157 · doi:10.1097/hnp.0000000000000401

What Chaplains Wish Nurses Knew

2020· article· en· W3048437157 on OpenAlexaff
Elizabeth Johnston Taylor, Myrna Trippon

Bibliographic record

VenueHolistic Nursing Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsGatekeepingNursingPsychologyQualitative researchMedicineSociology

Abstract

fetched live from OpenAlex

This report presents qualitative data from a larger study that sought to examine chaplain perspectives on collaboration and nurse-provided spiritual care. This cross-sectional, descriptive study used online survey methods to distribute an investigator-designed questionnaire to a convenience sample of members of the Association of Professional Chaplains (N = 298). Findings were generated by written responses to 3 open questions that were thematically analyzed, as well as 1 quantitative item. Over half of these chaplains reported they did experience nurse "gatekeeping" at least occasionally. Themes from qualitative data revealed chaplains perceive nurses: lack understanding about the role and abilities of chaplains, sometimes overstep their role or impede the work of chaplains, and allow personal "baggage" to influence their collaboration with chaplains. Likewise, however, respondents respected nurses and were eager to collaborate. Naming these challenges to nurse-chaplain collaboration allows nurses and chaplains to begin to address them.

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)0.016
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.129
GPT teacher head0.455
Teacher spread0.326 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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