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Record W4301400985 · doi:10.1186/s13063-022-06788-7

The BEACON study: an update to the protocol for a cohort study as part of an evaluation of the effectiveness of smartphone-assisted problem-solving therapy in men who present with intentional self-harm to emergency departments in Ontario

2022· article· en· W4301400985 on OpenAlexafffundabout
Simon Hatcher, Marnin J. Heisel, Oydeji Ayonrinde, Daniel J. Corsi, Nicole E. Edgar, Sidney H. Kennedy, Sakina J. Rizvi, Ayal Schaffer, Mark Sinyor

Bibliographic record

VenueTrials · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoKingston Health Sciences CentreQueen's UniversityParkwood InstituteSt. Michael's HospitalWestern UniversityCanada Research ChairsOttawa HospitalUniversity of Ottawa
FundersOntario SPOR SUPPORT Unit
KeywordsMedicineProtocol (science)Smartphone applicationMedical physicsCohort studyCohortPhysical therapyAlternative medicinePathologyComputer scienceMultimedia

Abstract

fetched live from OpenAlex

BACKGROUND: Men who present to the emergency department (ED) with self-harm are at high risk of dying by suicide, with 2.7% of men dying in the year following their presentation, more than double the rate for women (1.2%). Despite this, care received after an ED visit is highly variable and many are not assessed for psychological needs. Furthermore, the limited psychological care that is available is often not covered by provincial health insurance. Even when referrals for follow-up care are made, engagement rates are low. Previous recommendations to improve engagement include written discharge plans, caring contacts, and focused interventions targeting middle-aged men at elevated risk of dying by suicide. Blended care, the incorporation of technology into traditional care, has also been proposed as a method to increase engagement in and clinical benefits from psychotherapy. This project aims to determine whether the delivery of an evidence based treatment (problem-solving therapy (PST)) is enhanced by the addition of a custom smartphone application (BEACON) compared to usual care. Due to the impact of the COVID-19 pandemic on site participation and the planned implementation, we have made several changes to the study design, primary outcome, and implementation. METHOD: We originally proposed a cohort study nested within a larger cluster randomized trial wherein intervention sites would deliver the blended care, and control sites, whose personnel were not aware of their participation, would continue delivering usual care. The cohort study evaluated participant level outcomes as previously described by Hatcher et al. (2020). Due to pandemic-related constraints, our number of participating sites dropped to five potential sites which left the cohort study underpowered. As such, we changed the study design to a multi-site, individual randomized controlled trial (RCT) among the five remaining sites. Participants will be randomized to six sessions of therapy (PST) alone, or to the therapy plus BEACON, and followed up for 6 months. Our primary outcome was changed to evaluate feasibility and acceptability with the aim of designing a definitive RCT. Study implementation was reimagined to allow for completely virtual/online conduct to comply with local COVID-19 and institutional restrictions on in-person activities. CONCLUSION: This updated protocol will provide strong results for the planning of a definitive RCT of the blended care intervention in the future, addressing areas of difficulty and concern prior to its implementation. We will evaluate the feasibility of the study intervention, assess recruitment and retention of participants, and address challenges with implementing the protocol. Lastly, we will evaluate the appropriateness of our primary outcome measure and accurately determine a sample size for a definitive RCT. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03473535 . Registered on March 22, 2018.

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)0.025
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 score0.386
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.112
GPT teacher head0.431
Teacher spread0.319 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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