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Record W3134458590 · doi:10.31581/jbs-28.1-2.4(2018)

Thankful in Adversity

2018· article· en· W3134458590 on OpenAlexvenueno aff
Lindsay-Rose Dykema

Bibliographic record

VenueThe Journal of Bahá’í Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyTheme (computing)FaithMental illnessSchizophrenia (object-oriented programming)PsychotherapistSpiritualityDimension (graph theory)Personal developmentPsychiatryMedicineAlternative medicineEpistemology

Abstract

fetched live from OpenAlex

Mental health recovery has been conceptualized as a process through which individuals with severe mental illness improve their health and wellness, live a self-directed life, and work toward meaningful personal goals. While this is clearly an improvement over the traditional medical model of treatment of schizophrenia and related disorders, the spiritual dimension of mental health recovery still warrants closer investigation. The idea that adversity may offer spiritual insight and opportunities for personal growth—a common theme in the teachings of the Bahá’í Faith—is particularly worthy of consideration. This paper reflects on how both the Bahá’í Writings and the literature on benefit finding can enhance the understanding and applications of mental health recovery principles.

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)0.004
metaresearch head score (Gemma)0.019
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: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0090.009
Scholarly communication0.0040.005
Open science0.0010.007
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0150.006

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 head0.096
GPT teacher head0.425
Teacher spread0.329 · 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
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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