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Record W2802942275 · doi:10.2196/10323

A Hybrid Web-Based and In-Person Self-Management Intervention Aimed at Preventing Acute to Chronic Pain Transition After Major Lower Extremity Trauma: Feasibility and Acceptability of iPACT-E-Trauma

2018· article· en· W2802942275 on OpenAlexafffundvenue
Mélanie Berube, Céline Gélinas, Nancy Feeley, Géraldine Martorella, José Côté, G. Yves Laflamme, Dominique M. Rouleau, Manon Choinière

Bibliographic record

VenueJMIR Formative Research · 2018
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsJewish General HospitalCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersCanadian Institutes of Health ResearchRéseau québécois de recherche sur la douleur
KeywordsMedicinePhysical therapyContext (archaeology)Intervention (counseling)Chronic painPsychological interventionCoachingPopulationTrauma centerNursingPsychologyInternal medicineRetrospective cohort study

Abstract

fetched live from OpenAlex

BACKGROUND: A transition from acute to chronic pain frequently occurs after major lower extremity trauma. While the risk factors for developing chronic pain in this population have been extensively studied, research findings on interventions aiming to prevent chronic pain in the trauma context are scarce. Therefore, we developed a hybrid, Web-based and in-person, self-management intervention to prevent acute to chronic pain transition after major lower extremity trauma (iPACT-E-Trauma). OBJECTIVE: This study aimed to assess the feasibility and acceptability of iPACT-E-Trauma. METHODS: Using a descriptive design, the intervention was initiated at a supra-regional level-1 trauma center. Twenty-eight patients ≥18 years old with major lower extremity trauma, presenting with moderate to high pain intensity 24 hours post-injury were recruited. Feasibility assessment was two-fold: 1) whether the intervention components could be provided as planned to ≥80% of participants and 2) whether ≥80% of participants could complete the intervention. The rates for both these variables were calculated. The E-Health Acceptability Questionnaire and the Treatment Acceptability and Preference Questionnaire were used to assess acceptability. Mean scores were computed to determine the intervention's acceptability. RESULTS: More than 80% of participants received the session components relevant to their condition. However, the Web pages for session 2, on the analgesics prescribed, were accessed by 71% of participants. Most sessions were delivered according to the established timeline for ≥80% of participants. Session 3 and in-person coaching meetings had to be provider earlier for ≥35% of participants. Session duration was 30 minutes or less on average, as initially planned. More than 80% of participants attended sessions and <20% did not apply self-management behaviors relevant to their condition, with the exception of deep breathing relaxation exercises which was not applied by 40% of them. Web and in-person sessions were assessed as very acceptable (mean scores ≥3 on a 0 to 4 descriptive scale) across nearly all acceptability attributes. CONCLUSIONS: Findings showed that the iPACT-E-Trauma intervention is feasible and was perceived as highly acceptable by participants. Further tailoring iPACT-E-Trauma to patient needs, providing more training time for relaxation techniques, and modifying the Web platform to improve its convenience could enhance the feasibility and acceptability of the intervention. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 91987302; http://www.controlled-trials.com/ISRCTN91987302 (Archived by WebCite at http://www.webcitation.org/6ynibjPHa).

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.371
Teacher spread0.342 · 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 designNon-randomized trial
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

Citations10
Published2018
Admission routes3
Has abstractyes

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