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Record W2660739931 · doi:10.5498/wjp.v7.i2.89

Development of an instrument to measure patients’ attitudes towards involuntary hospitalization

2017· article· en· W2660739931 on OpenAlexafffund
Adel Gabriel

Bibliographic record

VenueWorld Journal of Psychiatry · 2017
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsFace validityMedicineContent validityConstruct validityScale (ratio)Internal consistencyReliability (semiconductor)Concurrent validityConvergent validityRating scalePsychometricsClinical psychologyPsychiatryPsychology

Abstract

fetched live from OpenAlex

AIM: To construct and assess the psychometric properties of an instrument to measure patients' attitudes towards involuntary hospitalization. METHODS: = 15) were invited to participate in the validation process of the written instrument, by formally rating each item of the instrument for its relevancy in measuring patients' attitudes to involuntary admission. In the second phase of the project, the instrument was administered to a sample of eighty consecutive patients, who were admitted involuntarily to an acute psychiatric unit of a teaching hospital. All patients completed the constructed attitudes towards involuntary admission scale, and the client satisfaction questionnaire. RESULTS: Responses from psychiatry and advocacy experts provided evidence for face and content validity for the constructed instrument. The internal consistency reliability of the instrument is 0.84 (Chronbach' alpha), factor analysis resulted in three correlated, and theoretically meaningful factors. There was evidence for content, convergent, and concurrent validity. CONCLUSION: A reliable twenty one item instrument scale to measure patients' attitudes to involuntary admission was developed. The developed instrument has high reliability, there is strong evidence for validity, and it takes ten minutes to complete.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.366
Teacher spread0.321 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

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