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Record W4283805281 · doi:10.2196/24376

Mobile Videoconferencing for Occupational Therapists’ Assessments of Patients’ Home Environments Prior to Hospital Discharge: Mixed Methods Feasibility and Comparative Study

2022· article· en· W4283805281 on OpenAlexaffvenue
Karine Latulippe, Dominique Giroux, Manon Guay, Dahlia Kairy, Claude Vincent, Katia Boivin, Ernesto Morales, Nataša Obradović, Véronique Provencher

Bibliographic record

VenueJMIR Aging · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsQuebec Rehabilitation Research NetworkUniversité de SherbrookeUniversité de MontréalUniversité LavalQuebec Network for Research on AgingMcGill UniversityCentre hospitalier universitaire de QuébecCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsVideoconferencingTelehealthMedicineNursingOccupational therapyFamily caregiversTelemedicineFamily medicinePhysical therapyHealth careMultimedia

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational therapists who work in hospitals need to assess patients' home environment in preparation for hospital discharge in order to provide recommendations (eg, technical aids) to support their independence and safety. Home visits increase performance in everyday activities and decrease the risk of falls; however, in some countries, home visits are rarely made prior to hospital discharge due to the cost and time involved. In most cases, occupational therapists rely on an interview with the patient or a caregiver to assess the home. The use of videoconferencing to assess patients' home environments could be an innovative solution to allow better and more appropriate recommendations. OBJECTIVE: The aim of this study was (1) to explore the added value of using mobile videoconferencing compared with standard procedure only and (2) to document the clinical feasibility of using mobile videoconferencing to assess patients' home environments. METHODS: Occupational therapists assessed home environments using, first, the standard procedure (interview), and then, videoconferencing (with the help of a family caregiver located in patients' homes, using an electronic tablet). We used a concurrent mixed methods design. The occupational therapist's responsiveness to telehealth, time spent on assessment, patient's occupational performance and satisfaction, and major events influencing the variables were collected as quantitative data. The perceptions of occupational therapists and family caregivers regarding the added value of using this method and the nature of changes made to recommendations as a result of the videoconference (if any) were collected as qualitative data, using questionnaires and semistructured interviews. RESULTS: Eight triads (6 occupational therapists, 8 patients, and 8 caregivers) participated. The use of mobile videoconferencing generally led occupational therapists to modify the initial intervention plan (produced after the standard interview). Occupational therapists and caregivers perceived benefits in using mobile videoconferencing (eg, the ability to provide real-time comments or feedback), and they also perceived disadvantages (eg, videoconferencing requires additional time and greater availability of caregivers). Some occupational therapists believed that mobile videoconferencing added value to assessments, while others did not. CONCLUSIONS: The use of mobile videoconferencing in the context of hospital discharge planning has raised questions of clinical feasibility. Although mobile videoconferencing provides multiple benefits to hospital discharge, including more appropriate occupational therapist recommendations, time constraints made it more difficult to perceive the added value. However, with smartphone use, interdisciplinary team involvement, and patient participation in the videoconference visit, mobile videoconferencing can become an asset to hospital discharge planning. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11674.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.199
GPT teacher head0.565
Teacher spread0.366 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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