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Development and Psychometric Properties of the Family Life Interview

2009· article· en· W2122928504 on OpenAlexaff
Gwynnyth Llewellyn, Anita Bundy, Rachel Mayes, David McConnell, Eric Emerson, Jennie Brentnall

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2009
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of Alberta
FundersAustralian Research Council
KeywordsRasch modelPsychologyLogistic regressionConstruct (python library)Scale (ratio)Psychological interventionTest (biology)Clinical psychologyConstruct validityApplied psychologyPsychometric testingPsychometricsDevelopmental psychologyMedicinePsychiatryComputer science

Abstract

fetched live from OpenAlex

Background This study describes the development and trialling of the Family Life Interview (FLI), a clinical tool designed to examine sustainability of family routines. Materials and Methods The FLI, a self‐report instrument completed by a parent within a semi‐structured practitioner – parent interview, was administered to 118 parents, with re‐test interviews being conducted with 39 parents. Rasch analysis was used to examine scale structure, evidence for construct validity and precision of measurement of the FLI items. Logistic regression was used to explore the contribution of the FLI to predicting out‐of‐home placement scores. Results The FLI produced valid data on the sustainability of family routines. The FLI was found to be useful for predicting families at risk of seeking out‐of‐home placement driven by crisis. Conclusions The FLI offers practitioners a psychometrically sound instrument designed to illuminate the particularity of each family’s circumstances, critical to developing interventions for increasing the sustainability of family routines.

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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.379
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.304
GPT teacher head0.405
Teacher spread0.101 · 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 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

Citations18
Published2009
Admission routes1
Has abstractyes

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