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Record W2083749375 · doi:10.1177/0269216308094519

Continued study of the psychometric properties of the McGill quality of life questionnaire

2008· article· en· W2083749375 on OpenAlexaffabout
Mélissa Henry, LN Huang, MK Ferland, Jennifer Mitchell, S. Robin Cohen

Bibliographic record

VenuePalliative Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsDisk formattingQuality of life (healthcare)Quality (philosophy)Scale (ratio)MedicinePsychometricsPsychologyClinical psychologyApplied psychologyComputer scienceNursingPhysics

Abstract

fetched live from OpenAlex

The McGill Quality of Life Questionnaire (MQOL) is a widely used tool that has been specifically developed to measure the quality of life of patients facing a life-threatening illness. Preferably, a self-report instrument has an equal number of items worded positively and negatively. However, all the psychological scales of the MQOL are worded so that a high score is negative, whereas the existential scales are worded so that a high score is positive. The goal of this study was to investigate the influence of MQOL item formatting on patient responses. In order to do so, a modified version of the questionnaire was distributed to and completed by 205 patients in two oncology clinics. The modified version had an equal amount of items worded in a positive direction and negative direction in each of the domains. Results of this study were found to be different from those of other studies: the loading of the items was partly based on scale direction. These changes support the idea that the MQOL formatting has some impact on patient responses. However, factors were also determined by content. Given that MQOL has been widely used and the original formatting provides conceptually clearer subscales, we suggest maintaining the original format, keeping in mind the effect of formatting when interpreting scores.

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)0.003
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 score0.010
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.129
GPT teacher head0.348
Teacher spread0.219 · 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

Citations17
Published2008
Admission routes2
Has abstractyes

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