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Mediation of the Relationship between Inner Voice Experiences and Health-Related Quality of Life

2005· article· en· W2151909674 on OpenAlexaff
Margaret England

Bibliographic record

VenuePerspectives In Psychiatric Care · 2005
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMediationPsychologyPerceptionQuality of life (healthcare)Exploratory researchQuality (philosophy)Developmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

PROBLEM: Appraisals tied to voice hearing and other subjective experiences pose serious challenges for nursing because of their implications for health and safety of voice hearers and others. METHOD: An exploratory, correlational design involving hierarchical analysis of data from 337 voice hearers. FINDINGS: Inner voice experiences and subjective-deficit symptoms each had significant negative, independent effects on perceptions of the integrity of the functional nervous system, self-esteem, and health-related quality of life. The combination of subjective-deficit symptoms, perceptions of the integrity of the functional nervous system and self-esteem significantly reduced the negative impact of inner voice experiences on health related quality of life (R2 = .511). Perceived integrity of the functional nervous system and self-esteem together almost completely attenuated the impact of voice hearing on subjects' health-related quality of life. CONCLUSION: These results highlight the need to develop strategies that can help voice hearers respond to inner experiences in a more positive manner.

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)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.095
GPT teacher head0.420
Teacher spread0.325 · 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 designObservational
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
Published2005
Admission routes1
Has abstractyes

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