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Record W4384405636 · doi:10.1080/02699052.2023.2230873

Preliminary efficacy and predictors of response to a remotely-delivered symptom self-management program for persistent symptoms after concussion

2023· article· en· W4384405636 on OpenAlexafffund
Liesel-Ann Meusel, Brenda Colella, Lesley Ruttan, Maria Carmela Tartaglia, Robin Green

Bibliographic record

VenueBrain Injury · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMoodAnxietyMedicinePhysical therapyQuality of life (healthcare)Depression (economics)CognitionConfidence intervalSelf-managementStress managementConcussionInjury preventionPoison controlClinical psychologyPsychiatryInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

Background More than a quarter of adults with concussion endure prolonged symptoms of >3 months. We developed the Concussion Education Self-Management program to help people manage persisting symptoms. Here, we assess feasibility, preliminary efficacy, and correlates of response.Methods N = 80 adults participated in the program; ages ranged from 18 to 65 years and time post-injury ranged from 6 months to 18 years. Weekly sessions, delivered remotely and in groups, comprised education and strategies for management of cognitive, emotional, and physical symptoms. Primary outcome: Confidence to self-manage symptoms. Secondary outcomes: Quality of life; mood/anxiety/stress. Predictors of response: Self-reported cognitive, emotional and physical symptoms at intake.Results Pre- to post-program improvements were observed in confidence to self-manage, p < 0.03; quality of life, p < 0.001; depression, p < 0.001; anxiety, p < 0.001; and stress, p < 0.001. Considering confidence to self-manage, those with fewer cognitive and physical symptoms benefitted more (p’s < 0.0005 and p < 0.01, respectively).Discussion This program shows promise for improving self-management of prolonged symptoms. Those with high symptom burden may need extra sessions to benefit. This is a cost-effective and scalable program that can reach people regardless of geographic location or impediments to travel.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.876
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.025
GPT teacher head0.330
Teacher spread0.306 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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