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Record W1998631695 · doi:10.1111/nin.12065

Host and guest: an applied hermeneutic study of mental health nurses' practices on inpatient units

2014· article· en· W1998631695 on OpenAlexaff
Graham McCaffrey

Bibliographic record

VenueNursing Inquiry · 2014
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMetaphorLived experienceTheme (computing)Value (mathematics)Mental healthHierarchyReading (process)Identity (music)HermeneuticsPsychologyNursingSociologyMedicineEpistemologyAestheticsPsychoanalysisPsychotherapistLinguisticsComputer science

Abstract

fetched live from OpenAlex

The metaphor of host and guest has value for exploring the practice and role identity of nurses on inpatient mental health units. Two complementary texts, one from the ancient Zen record of Lin-chi, and the other from the contemporary hermeneutic philosopher Richard Kearney, are used to elaborate meanings of host and guest that can be applied to the situation of mental health nurses. In a doctoral study with a hermeneutic design, I addressed the topic of nurse-patient relationship using an interpretive framework that included sources from Buddhist thought. The positions of host and guest emerged from interviews with nurses as one interpretive theme to open up new understanding of the topic. The two texts, originally distant in era and culture, both employ the host and guest metaphor. They are applied to extracts from interviews to open up discussions of hierarchy, status, patients' perspectives, otherness and resistances as features of nurses' complex experience. These provide insights into understanding practice and suggest implications for how institutional environments shape practice. An intercultural reading of texts can provide a source of new understanding of nurse-patient relationships.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.182
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

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

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 head0.073
GPT teacher head0.398
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 teacher head, 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

Citations2
Published2014
Admission routes1
Has abstractyes

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