A qualitative evaluation of the implementation of a pre-consultation tool for older adults in primary care
Bibliographic record
Abstract
Context: Pre-consultation telemedicine tools have been proposed as an effective solution to optimize the care of the growing number of older patients with chronic conditions. However, little information exists on how to successfully implement these tools in the primary care setting. The ESOGER tool is a telemedicine tool in the form of a telephone assessment questionnaire for individuals aged 65 and over. It gathers contextual information on physical, cognitive, mental, and social health, and generates an automated summary report of potential vulnerabilities. Objective: To determine the barriers and facilitators to the implementation of a pre-consultation tool tailored for older adults in primary care. Study design: A qualitative descriptive study design was utilized. The tool was implemented and tested in four university-affiliated family medicine groups in Quebec, Canada from December 2021 to October 2022. Semi-structured interviews were conducted with a total of 26 participants, including clinicians, administrators, and other clinic personnel, between May and October 2022. Analysis: Analysis of the transcribed interviews and internal logs was approached through a reflective thematic analysis method, drawing from the RE-AIM conceptual framework (Reach Effectiveness Adoption Implementation Maintenance). The Consolidated Framework for Implementation Research (CFIR) guided the thematic development process. Setting or dataset: Community-based practice. Population studied: Older adults aged 65 years and above. Intervention/Instrument (83 characters): Administration of the ESOGER tool prior to the visit of older adults at the participating clinics. Results: We identified 5 barriers/facilitators related to the implementation of ESOGER. First, the use of ESOGER was facilitated when it was administered by a clinician (1). Coordinating and communicating within the team and with the research team (2), as well as identifying key resources to reach participating clinicians (3), were both barriers and facilitators to the implementation. The lack of context specific adaptation to the organizational structure of each FMG (4) and the availability of resources to administer ESOGER (5) were barriers to both one-time and long-term implementation of the tool. Conclusion: This study found that while the use of a pre-consultation tool can be feasible under the right conditions, organizational barriers must be considered for successful long-term implementation.
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 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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".