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Record W4396776211 · doi:10.2196/52801

Feasibility of a 2-Part Substance Use Screener Self-Administered by Patients on Paper: Observational Study

2024· article· en· W4396776211 on OpenAlexvenueno aff
Joanna L. Kramer, Timothy E. Wilens, Vinod Rao, Richard Villa, Amy M. Yule

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institutes of Health
KeywordsObservational studySubstance usePsychologyMedicineClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Measurement-based care in behavioral health uses patient-reported outcome measures (PROMs) to screen for mental health symptoms and substance use and to assess symptom change over time. While PROMs are increasingly being integrated into electronic health record systems and administered electronically, paper-based PROMs continue to be used. It is unclear if it is feasible to administer a PROM on paper when the PROM was initially developed for electronic administration. OBJECTIVE: This study aimed to examine the feasibility of patient self-administration of a 2-part substance use screener-the Tobacco, Alcohol, Prescription medications, and other Substances (TAPS)-on paper. This screener was originally developed for electronic administration. It begins with a limited number of questions and branches to either skip or reflex to additional questions based on an individual's responses. In this study, the TAPS was adapted for paper use due to barriers to electronic administration within an urgent care behavioral health clinic at an urban health safety net hospital. METHODS: From August 2021 to March 2022, research staff collected deidentified paper TAPS responses and tracked TAPS completion rates and adherence to questionnaire instructions. A retrospective chart review was subsequently conducted to obtain demographic information for the patients who presented to the clinic between August 2021 and March 2022. Since the initial information collected from TAPS responses was deidentified, demographic information was not linked to the individual TAPS screeners that were tracked by research staff. RESULTS: A total of 507 new patients were seen in the clinic with a mean age of 38.7 (SD 16.6) years. In all, 258 (50.9%) patients were male. They were predominantly Black (n=212, 41.8%), White (n=152, 30%), and non-Hispanic or non-Latino (n=403, 79.5%). Most of the patients were publicly insured (n=411, 81.1%). Among these 507 patients, 313 (61.7%) completed the TAPS screener. Of these 313 patients, 76 (24.3%) adhered to the instructions and 237 (75.7%) did not follow the instructions correctly. Of the 237 respondents who did not follow the instructions correctly, 166 (70%) answered more questions and 71 (30%) answered fewer questions than required in TAPS part 2. Among the 237 patients who did not adhere to questionnaire instructions, 44 (18.6%) responded in a way that contradicted their response in part 1 of the screener and ultimately affected their overall TAPS score. CONCLUSIONS: It was challenging for patients to adhere to questionnaire instructions when completing a substance use screener on paper that was originally developed for electronic use. When selecting PROMs for measurement-based care, it is important to consider the structure of the questionnaire and how the PROM will be administered to determine if additional support for PROM self-administration needs to be implemented.

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)0.012
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.280
GPT teacher head0.460
Teacher spread0.180 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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