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Record W2276349113 · doi:10.11575/jah.v0i0.53272

Right There, in the Midst of It: Impacts of the Therapeutic Relationship on Mental Health Nurses

2016· article· en· W2276349113 on OpenAlexaff
Angela C Morck

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMental healthNarrativeTherapeutic relationshipNursingPsychologyOrder (exchange)PsychotherapistSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Mental health nurses are frequently confronted by intense emotions within the therapeutic relationship. In this philosophical hermeneutic inquiry, five mental health nurses were interviewed to extend our understandings of how nurses are impacted by the interplay with the often emotion-laden narratives of their patients. Findings exposed the nurses journeyed between fluctuating needs to separate and protect their private from their work life. In order for this fluctuation to occur, they developed a sense of the world as requiring a sanctuary. This ontological place of home is the extent to which they felt safe and sheltered in order for this process of awareness of self as person and self as nurse to unfold. This research brings to the forefront the ways in which mental health nursing practice, education, and research are reciprocally moved by the practical day-to-day activities of being in therapeutic 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 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)0.014
metaresearch head score (Gemma)0.027
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 score0.029
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0290.027
Scholarly communication0.0140.009
Open science0.0020.018
Research integrity0.0040.008
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 head0.316
GPT teacher head0.619
Teacher spread0.303 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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