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Record W2752308437 · doi:10.2196/mental.6805

Assessing the Equivalence of Paper, Mobile Phone, and Tablet Survey Responses at a Community Mental Health Center Using Equivalent Halves of a ‘Gold-Standard’ Depression Item Bank

2017· article· en· W2752308437 on OpenAlexvenueno aff
Benjamin B. Brodey, Nicole Gonzalez, Kathryn Ann Elkin, William J. Sasiela, Inger Sigrun Brodey

Bibliographic record

VenueJMIR Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institutes of HealthAlliance Healthcare Foundation
KeywordsMental healthItem bankItem response theoryPatient-Reported Outcomes Measurement Information SystemPopulationMobile phoneModalitiesMedicinePhonePopularityDepression (economics)PsychologyApplied psychologyPsychiatryClinical psychologyComputerized adaptive testingPsychometricsComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The computerized administration of self-report psychiatric diagnostic and outcomes assessments has risen in popularity. If results are similar enough across different administration modalities, then new administration technologies can be used interchangeably and the choice of technology can be based on other factors, such as convenience in the study design. An assessment based on item response theory (IRT), such as the Patient-Reported Outcomes Measurement Information System (PROMIS) depression item bank, offers new possibilities for assessing the effect of technology choice upon results. OBJECTIVE: To create equivalent halves of the PROMIS depression item bank and to use these halves to compare survey responses and user satisfaction among administration modalities-paper, mobile phone, or tablet-with a community mental health care population. METHODS: The 28 PROMIS depression items were divided into 2 halves based on content and simulations with an established PROMIS response data set. A total of 129 participants were recruited from an outpatient public sector mental health clinic based in Memphis. All participants took both nonoverlapping halves of the PROMIS IRT-based depression items (Part A and Part B): once using paper and pencil, and once using either a mobile phone or tablet. An 8-cell randomization was done on technology used, order of technologies used, and order of PROMIS Parts A and B. Both Parts A and B were administered as fixed-length assessments and both were scored using published PROMIS IRT parameters and algorithms. RESULTS: All 129 participants received either Part A or B via paper assessment. Participants were also administered the opposite assessment, 63 using a mobile phone and 66 using a tablet. There was no significant difference in item response scores for Part A versus B. All 3 of the technologies yielded essentially identical assessment results and equivalent satisfaction levels. CONCLUSIONS: Our findings show that the PROMIS depression assessment can be divided into 2 equivalent halves, with the potential to simplify future experimental methodologies. Among community mental health care recipients, the PROMIS items function similarly whether administered via paper, tablet, or mobile phone. User satisfaction across modalities was also similar. Because paper, tablet, and mobile phone administrations yielded similar results, the choice of technology should be based on factors such as convenience and can even be changed during a study without adversely affecting the comparability of results.

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)0.039
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.116
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.135
GPT teacher head0.496
Teacher spread0.362 · 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.

Study designObservational
DomainMethods
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

Citations14
Published2017
Admission routes1
Has abstractyes

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