Understanding the Experience of Geriatric Care Professionals in Using Telemedicine to Care for Older Patients in Response to the COVID-19 Pandemic: Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Geriatric care professionals were forced to rapidly adopt the use of telemedicine technologies to ensure the continuity of care for their older patients in response to the COVID-19 pandemic. However, there is little current literature that describes how telemedicine technologies can best be employed to meet the needs of geriatric care professionals in providing care to frail older patients and their caregivers, and families. OBJECTIVE This study aimed to identify the benefits and challenges geriatric care professionals face when using telemedicine technologies with frail older patients, their caregivers, and families, and how to maximize the benefits of this method of providing care. METHODS We conducted a mixed methods study that recruited geriatric care professionals to complete an online survey regarding their personal demographics and experiences with using telemedicine and participate in a semi-structured interview. Interview responses were analyzed using the Consolidated Framework for Implementation Research (CFIR). RESULTS We obtained quantitative and qualitative data from 30 practicing geriatric care professionals (22 geriatricians, 5 geriatric psychiatrists, and 3 geriatric specialist nurses) recruited from across the Greater Toronto Area. Analysis of interview data identified 5 CFIR contextual barriers (Complexity, Design quality and packaging, Patient needs and resources, Readiness for implementation, and Culture) and 13 CFIR contextual facilitators (Relative Advantage, Adaptability, Tension for Change, Available Resources, Access to Knowledge, Network and Communications, Compatibility, Knowledge and Beliefs, Self-Efficacy, Champions, External Agents, Executing, and Reflecting and Evaluating). The CFIR concept of External Policy and Incentives was found to be a neutral construct. CONCLUSIONS This is the first known study to use the CFIR to develop a comprehensive narrative to characterize the experiences of geriatric care professionals using telemedicine technologies in providing care. Overall, telemedicine can significantly enable most of the geriatric care that is traditionally provided in person, but is less useful in providing specific aspects of geriatric care to frail older patients and their caregivers, and families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".