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Record W3037633155 · doi:10.3390/ijerph17124583

Addressing Delusions in Women and Men with Delusional Disorder: Key Points for Clinical Management

2020· review· en· W3037633155 on OpenAlexaff
Alexandre González-Rodríguez, Mary V. Seeman

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typereview
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDelusionPsychologyDelusional disorderKey (lock)PsychiatryClinical psychologyMedicinePsychosisComputer scienceComputer security

Abstract

fetched live from OpenAlex

Delusional disorders (DD) are difficult conditions for health professionals to treat successfully. They are also difficult for family members to bear. The aim of this narrative review is to select from the clinical literature the psychosocial interventions that appear to work best for these conditions and to see whether similar strategies can be modeled or taught to family members so that tensions at home are reduced. Because the content of men's and women's delusions sometimes differ, it has been suggested that optimal interventions for the two sexes may also differ. This review explores three areas: (a) specific treatments for men and women; (b) recommended psychological approaches by health professionals, especially in early encounters with patients with DD; and (c) recommended psychoeducation for families. Findings are that there is no evidence for differentiated psychosocial treatment for men and women with delusional disorder. What is recommended in the literature is to empathically elicit the details of the content of delusions, to address the accompanying emotions rather than the logic of the presented argument, to teach self-soothing techniques, and to monitor behavior with respect to its safety. These recommendations have only been validated in individual patients and families. More rigorous clinical trials need to be conducted.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.309
GPT teacher head0.568
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2020
Admission routes1
Has abstractyes

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