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Record W2791330820 · doi:10.1080/08039488.2018.1435721

A confirmative clinimetric analysis of the 36-item Family Assessment Device

2018· article· en· W2791330820 on OpenAlexfundno aff
Nina Timmerby, Fiammetta Cosci, Maggie Watson, Cláudio Csillag, Florence Schmitt, Barbara Steck, Per Bech, Mikael Thastum

Bibliographic record

VenueNordic Journal of Psychiatry · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersEuropean CommissionTrygFondenMcMaster University
KeywordsPsychologyPsychiatryClinical psychologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: The Family Assessment Device (FAD) is a 60-item questionnaire widely used to evaluate self-reported family functioning. However, the factor structure as well as the number of items has been questioned. A shorter and more user-friendly version of the original FAD-scale, the 36-item FAD, has therefore previously been proposed, based on findings in a nonclinical population of adults. AIMS: We aimed in this study to evaluate the brief 36-item version of the FAD in a clinical population. METHODS: Data from a European multinational study, examining factors associated with levels of family functioning in adult cancer patients' families, were used. Both healthy and ill parents completed the 60-item version FAD. The psychometric analyses conducted were Principal Component Analysis and Mokken-analysis. RESULTS: A total of 564 participants were included. Based on the psychometric analysis we confirmed that the 36-item version of the FAD has robust psychometric properties and can be used in clinical populations. CONCLUSIONS: The present analysis confirmed that the 36-item version of the FAD (18 items assessing 'well-being' and 18 items assessing 'dysfunctional' family function) is a brief scale where the summed total score is a valid measure of the dimensions of family functioning. This shorter version of the FAD is, in accordance with the concept of 'measurement-based care', an easy to use scale that could be considered when the aim is to evaluate self-reported family functioning.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.354
Teacher spread0.330 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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