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Record W4309092803 · doi:10.2147/cia.s384822

Barriers and Enablers to the Use of Web-Based Applications for Older Adults and Their Caregivers Post-Hip Fracture Surgery: A Descriptive Qualitative Study

2022· article· en· W4309092803 on OpenAlexafffundabout
Chantal Backman, Steve Papp, Anne Harley, Sandra Houle, Yeabsira Mamo, Stéphane Poitras, Soha Shah, Randa Berdusco, Paul E. Beaulé, Véronique French-Merkley

Bibliographic record

VenueClinical Interventions in Aging · 2022
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsOttawa HospitalBruyèreUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMedicineRehabilitationQualitative researchHip fractureDescriptive statisticsNursingIntervention (counseling)Descriptive researchGerontologyPhysical therapyOsteoporosis

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to describe the barriers and enablers to the use of web-based applications designed to help manage the personalized needs of older adults and their caregivers post-hip fracture surgery while transitioning from hospital to geriatric rehabilitation to home. Methods: This was a descriptive qualitative study consisting of semi-structured interviews informed by the Theoretical Domains Framework. The study took place between March 2021 and April 2022 on an orthopaedic unit in a large academic health sciences centre and in a geriatric rehabilitation service in Ontario, Canada. The transcripts were analyzed using a systematic 6-step approach. Results: Interviews were conducted with older adults (n = 10) and with caregivers (n = 8) post-hip fracture surgery. A total of 21 barriers and 24 enablers were identified. The top two barriers were a need for basic computer skills (n = 11, 61.1%) and a preference for direct verbal communication (n = 10, 55.6%). The top two enablers were having no concerns with using web-based applications (n = 12, 66.7%) and having ease of access to information (n = 10, 55.6%). Conclusion: We described the key barriers and enablers to the use of web-based applications from the perspectives of older adults and their caregivers. These factors will inform further developments of web-based applications aimed at improving the care transition from hospital to geriatric rehabilitation to home post-hip fracture surgery.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.141
GPT teacher head0.425
Teacher spread0.284 · 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 designQualitative
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

Citations5
Published2022
Admission routes3
Has abstractyes

Explore more

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