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Record W3027344481 · doi:10.1093/schbul/sbaa030.416

M104. THE USE OF SHORT MESSAGE SERVICE AS A MEANS OF CLINICAL ENGAGEMENT IN EARLY PSYCHOSIS

2020· article· en· W3027344481 on OpenAlexaff
Jessica D’Arcey, Aristotle N. Voineskos, Sean A. Kidd, Nicole Kozloff, George Foussias

Bibliographic record

VenueSchizophrenia Bulletin · 2020
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsDisengagement theoryShort Message ServiceMedicineAttendanceRandomized controlled trialIntervention (counseling)PsychiatryPsychosisGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Clinical disengagement of youth in early psychosis clinics continues to be a significant barrier to recovery as evidenced by high rates of treatment non-adherence (up to 60%) and clinic drop-out (30%). Disengagement from services results in longer durations of untreated/partially-treated illness, which in turn leads to higher rates of symptom burden, re-hospitalization, and poorer functional outcomes. Approaches aimed at improving engagement have typically hinged on efforts to increase access to clinicians either via telephone or in person, however financial and human resource limitations often undermine these efforts. This has caused a shift toward the use of SMS due to its low cost and popularity as 95% of youth in North America send and receive SMS messages daily. The current randomized controlled trial sought to evaluate the efficacy of a weekly SMS intervention delivered over nine months to improve engagement in early psychosis services. Methods 60 participants between the ages of 16 and 29 presenting with a first episode psychosis to an early psychosis clinic in an academic health centre were recruited for this study. Participants were randomized to either an active or sham SMS intervention arm, delivered weekly for nine months. Participants were blind to treatment allocation. The active SMS intervention consisted of weekly SMS messages with adaptive questions regarding general wellbeing, clinic attendance and medication adherence, while the sham SMS arm consisted of a weekly generic text message with no clinically relevant subject matter. Results All participants have been enrolled, with the final study visits to be completed in December 2019. Results presented will include analyses of efficacy of the active versus sham SMS intervention for improving service engagement, our primary outcome, consisting of self-reported medication adherence, clinician and client rated engagement, and attendance rates extracted from electronic medical charts. In addition, symptom and functional outcomes were assessed over the course of the nine month intervention across the active and sham intervention groups, which will also be presented. Feasibility data collected to date indicates a high degree of interest and acceptance by participants for the use of SMS as a means of engagement. Further, there have been no tolerability difficulties reported by participants to date. Discussion The high rates of disengagement from clinical services seen among youth and emerging adults with early psychosis indicate a need for effective interventions to promote engagement in treatment and support symptomatic and functional recovery for affected individuals. The results of this trial stand to offer insights into the potential efficacy of a simple weekly SMS intervention to promote engagement in clinical services for this population. The acceptability of this intervention by participants, coupled with its low cost and the popularity of SMS, suggest that this treatment approach, if effective, could be readily implemented within early psychosis services to support positive outcomes from an initial psychosis episode.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.166
GPT teacher head0.408
Teacher spread0.242 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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