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Record W4403384429 · doi:10.2196/53913

Understanding Experiences of Telehealth in Palliative Care: Photo Interview Study

2024· article· en· W4403384429 on OpenAlexvenueno aff
Mahima Kalla, Teresa Wulandari, Olivia Metcalf, Rashina Hoda, Xiao Chen, Andy Li, Catriona Parker, Michael Franco, Sam Georgy, Kit Huckvale, Christopher Bain, Peter Poon

Bibliographic record

VenueJMIR Human Factors · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintTelehealthInterviewPsychologyPalliative careTelemedicineMedicineMedical educationNursingSociologyHealth careComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: It is widely accepted that the COVID-19 pandemic has accelerated the era of online health care delivery, including within community palliative care. This study was part of a larger project involving a collaboration between universities, health care services, government agencies, and software developers that sought to enhance an existing telehealth (video call) platform with additional features to improve both patient and health care professional (HCP) experience in a palliative care context. Objective: The aim of this study was to understand palliative care patients' and HCPs' experiences of telehealth delivery in a palliative care context in Victoria, Australia. For the purposes of this study, telehealth included consultations by both video and telephone calls. By better understanding users' experiences and perceptions of telehealth, we hoped to determine users' preferences for new telehealth enhancement features. Methods: A total of 6 health care professionals and 6 patients were recruited from a major tertiary hospital network's palliative care unit in Victoria, Australia. Participants were asked to generate 3-5 photographs depicting their telehealth experiences. These photographs were used as visual aids to prompt discussion during subsequent one-on-one interviews. Intertextual analysis was conducted to identify key themes. Results: A total of 3 overarching themes emerged: comfort (or lack thereof) afforded by telehealth, connection considerations in telehealth, and care quality impacts of telehealth. Patients (n=6) described telehealth as supporting their physical and psychological comfort and maintaining connection with HCPs, yet there were specific situations where it failed to meet their needs or impacted care quality and delayed treatment. HCPs (n=6) recognized the benefit of telehealth for patients but reported several limitations of telehealth, in particular due to lack of physical examination opportunities. Participants indicated that 2 types of connection were imperative for effective telehealth delivery: technical connection (eg, good internet connectivity or clear phone line) and interpersonal connection (ie, good rapport and therapeutic alliance between the HCPs and patients). Often technical connection issues impeded the development of interpersonal connection between the HCPs and patients in telehealth. Conclusions: The findings presented in this study combined with other co-design activities, which are outside the scope of this paper, indicated the potential value of a telehealth enhancement feature that generates patient-facing clinical consultation summaries. Our team has developed a video telehealth enhancement feature (or "add-on"), which will enable clinicians to distill key actionable advice and self-management guidance discussed during teleconsultations for a take-home summary document for patients. The add-on's prototype has also been subjected to an initial simulation study, which will be reported in a future publication.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.277
GPT teacher head0.466
Teacher spread0.189 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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