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Record W2800824396 · doi:10.1177/247553031117a00407

Psoriasis Patients' Reflections on Provider Information Giving: Perceptions of Actual and Desired Experiences at Diagnosis

2011· article· en· W2800824396 on OpenAlexaff
Barat Wolfe, Ashley O’Toole, Jerry Tan, Fuschia M. Sirois

Bibliographic record

VenuePsoriasis Forum · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityBishop's UniversityWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsAcknowledgementPsoriasisPsychosocialPerceptionMedicineQuality of life (healthcare)DiseaseQualitative researchPsychologyFamily medicineNursingPsychiatryDermatologyPathologyComputer scienceSociology

Abstract

fetched live from OpenAlex

Background Psoriasis adversely impacts patients' quality of life, yet few studies have explored patients' experiences at diagnosis, a pivotal though largely negative time for any patient suffering from chronic disease. Objective Our objective was to identify potential shortcomings in the content and process of information delivery from the patient's perspective. Methods Twenty-three psoriasis patients completed an online survey regarding information they considered valuable at diagnosis and, in retrospect, content they would like to have received. Results Responses were subjected to a qualitative content analysis that revealed several themes: need for information and options, discussion of time and effort to find the right treatment, for providers to offer hope, and discussion of the psychosocial impact of psoriasis. Conclusions Many patients perceived negative actual experiences at diagnosis that were incongruent with their desired experiences. Psoriasis patients wanted information about disease and management, acknowledgement of psychosocial impact, and an empathetic mode of communication.

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)0.007
metaresearch head score (Gemma)0.033
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.188
GPT teacher head0.391
Teacher spread0.202 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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