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Record W4252455384 · doi:10.29173/jpnep13

"Be not Afraid:" A Statement of Fact and a Statement of Faith.

2021· article· en· W4252455384 on OpenAlexaff
Lawrence Onwuegbuchunam

Bibliographic record

VenueJournal of Practical Nurse Education and Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsColumbia CollegeAlberta Health Services
Fundersnot available
KeywordsSpiritualityAnxietyAmbivalenceFaithCLARITYMental healthPsychological interventionPsychotherapistPsychologyStatement (logic)Alternative medicinePsychiatryMedicineSocial psychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

The reality of ambivalence and resistance around the incorporation of spirituality and religious beliefs in the treatment of fear and anxiety disorders and other mental health conditions cannot be denied. The overall purpose of this scholarly article is to advance thoughtful deliberations and discussions around the possibility that healthy spirituality and healthy religious practices, together with psychotherapy and pharmacologic interventions, could be effective in the treatment of anxiety disorders and other mental health conditions. Through a review of scholarly articles and reflections on his experience in clinical nursing practice, the author demonstrates with clarity of thought, acute insight, and academic rigor the necessity and relevancy of including healthy spirituality and healthy religious beliefs, together with psychotherapy and pharmacology, in the management of fear and anxiety disorders and other mental health conditions. The article is a call for engagement in this issue, not a conquest.

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)0.009
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.071
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.019
Scholarly communication0.0070.012
Open science0.0010.006
Research integrity0.0090.023
Insufficient payload (model declined to judge)0.0090.004

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 head0.094
GPT teacher head0.507
Teacher spread0.414 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2021
Admission routes1
Has abstractyes

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