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Record W1498529600 · doi:10.5772/27696

Technology-Based Approaches to Improve Mental Health Outcomes for Patients with Traumatic Brain Injury

2012· book-chapter· en· W1498529600 on OpenAlexaff
Jane Topolovec‐Vranic, Svetlena Taneva, Justin Shamis, Donna Ouchterlony

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsTraumatic brain injuryMental healthPsychologyMedicinePhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

The sample sizes ranged from 48-786 patients with an average age of the participants from 30-50 years. Seven of the trials demonstrated efficacy of the intervention with effect sizes ranging from 0.42 to 0.65 as compared to placebo or wait list controls Only four of the depression-related websites reviewed were available in the English language: ODIN, MoodGYM, and Bluepages are publicly accessible on the web without charge to consumers and the fourth program, Sadness, is available under restricted license at a small cost. All of the programs employ at least some component of cognitive behavioural therapy (CBT). While some of these evaluations of technology-based treatment programs have demonstrated positive results on mental health outcomes, the extent of work in this area is limited when the focus is set on the TBI population. As summarized in the following section, very few technology-based resources have been developed and evaluated specifically for the TBI patient.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.124
GPT teacher head0.338
Teacher spread0.213 · 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.

Study designOther design
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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