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Record W4385684258 · doi:10.1093/ptj/pzad098

Development, Acceptability, and Usability of a Virtual Intervention for Vertebral Fractures

2023· article· en· W4385684258 on OpenAlexafffund
Matteo Ponzano, Nicholas Tibert, Sheila Brien, Larry Funnell, Jenna C. Gibbs, Heather Keller, Judi Laprade, Suzanne N. Morin, Αλεξάνδρα Παπαϊωάννου, Zachary J. Weston, Timothy H. Wideman, Lora Giangregorio

Bibliographic record

VenuePhysical Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of TorontoCanadian Society for Exercise PhysiologyResearch Institute for AgingImpactMcGill UniversityWilfrid Laurier UniversityUniversity of WaterlooUniversity of British ColumbiaInternational Collaboration On Repair DiscoveriesMcMaster UniversityOsteoporosis CanadaUniversity of British Columbia, Okanagan Campus
FundersInstitute of Musculoskeletal Health and ArthritisNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsUsabilityIntervention (counseling)Session (web analytics)Applied psychologyPhysical therapyMedicinePsychologyMedical educationComputer scienceNursingHuman–computer interaction

Abstract

fetched live from OpenAlex

OBJECTIVE: This project aimed to develop a virtual intervention for vertebral fractures (VIVA) to implement the international recommendations for the nonpharmacological management of osteoporotic vertebral fractures and to test its acceptability and usability. METHODS: VIVA was developed in accordance with integrated knowledge translation principles and was informed by the Behavioral Change Wheel, the Theoretical Domains Framework, and the affordability, practicability, effectiveness and cost-effectiveness, acceptability, side effects/safety, and equity (APEASE) criteria. The development of the prototype of VIVA involved 3 steps: understanding target behaviors, identifying intervention options, and identifying content and implementation options. The VIVA prototype was delivered to 9 participants to assess its acceptability and usability. RESULTS: VIVA includes 7 1-on-1 virtual sessions delivered by a physical therapist over 5 weeks. Each session lasts 45 minutes and is divided in 3 parts: education, training, and behavioral support/goal setting. Four main themes emerged from the acceptability evaluation: perceived improvements in pain, increased self-confidence, satisfaction with 1-on-1 sessions and resources, and ease of use. All of the participants believed that VIVA was very useful and were very satisfied with the 1-on-1 sessions. Four participants found the information received very easy to practice, 4 found it easy to practice, and 1 found it somewhat difficult to practice. Five participants were satisfied with the supporting resources, and 4 were very satisfied. Potential for statistically significant improvements was observed in participants' ability to make concrete plans about when, how, where, and how often to exercise. CONCLUSION: VIVA was acceptable and usable to the participants, who perceived improvements in pain and self-confidence. IMPACT: The virtual implementation of the recommendations for the nonpharmacological management of vertebral fractures showed high acceptability and usability. Future trials will implement the recommendations on a larger scale to evaluate their effectiveness.

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)0.010
metaresearch head score (Gemma)0.019
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 score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.367
Teacher spread0.338 · 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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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