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Record W4206931751 · doi:10.1007/s10461-022-03585-w

Utility and Impact of the Implementation of Same-Day, Self-administered Electronic Patient-Reported Outcomes Assessments in Routine HIV Care in two North American Clinics

2022· article· en· W4206931751 on OpenAlexaff
Duncan Short, Rob J. Fredericksen, Heidi M. Crane, Emma Fitzsimmons, Shivali Suri, Jean Bacon, Alexandra Musten, Kevin Gough, Moti Ramgopal, Jeff Berry, Abigail Kroch, Brenda Jacobs, Vince Hodge, Divya Korlipara, William B. Lober

Bibliographic record

VenueAIDS and Behavior · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsOntario HIV Treatment NetworkSt. Michael's Hospital
FundersNational Institute of Allergy and Infectious DiseasesViiV Healthcare
KeywordsMedicineHealth psychologyFamily medicineChartPublic healthSuicidal ideationHuman immunodeficiency virus (HIV)Outpatient clinicMedical emergencyNursingPoison controlSuicide prevention

Abstract

fetched live from OpenAlex

The PROgress study assessed the value and feasibility of implementing web-based patient-reported outcomes assessments (PROs) within routine HIV care at two North American outpatient clinics. People with HIV (PWH) completed PROs on a tablet computer in clinic before their routine care visit. Data collection included PROs from 1632 unique PWH, 596 chart reviews, 200 patient questionnaires, and 16 provider/staff questionnaires. During an initial setup phase involving 200 patients, PRO results were not delivered to providers; for all subsequent patients, providers received PRO results before the consultation. Chart review demonstrated that delivery of PRO results to providers improved patient-provider communication and increased the number of complex health and behavioral issues identified, recorded, and acted on, including suicidal ideation (88% with vs 38% without PRO feedback) and anxiety (54% with vs 24% without PRO feedback). In post-visit questionnaires, PWH (82%) and providers (82%) indicated that the PRO added value to the visit.

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)0.000
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 score0.075
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.442
Teacher spread0.416 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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