Comparing Patient and Provider Perspectives on a Primary Care Preconsultation Tool for Older Adults: a Qualitative Study
Bibliographic record
Abstract
Context: Pre-consultation tools are considered a promising way to support health providers and older adults in identifying multiple and often complex needs. However, few studies have evaluated preconsultation tools targeting older adults and healthcare professionals. Objective: This study compared the perspectives of patients and providers using ESOGER, a novel multidimensional assessment tool for older adults. Study design: Qualitative interviews were conducted with older adults (n=19) and health providers (n=17) in 4 family medicine clinics (2 rural and 2 urban) in Quebec, Canada. The recruitment of older adults was diversified according to age, gender, and comorbidity. Analysis: We completed a thematic inductive-deductive analysis of the interviews using the Dedoose software. Initial coding was based on the RE-AIM and Proctor et al. (2019) frameworks for implementation and evaluation. Setting and dataset: Community-based practice. Population studied: Older adults aged 65 years and above. Intervention/Instrument: Administration of the ESOGER tool prior to the visit of older adults at the participating clinics. Results: We divided the results into 4 categories: acceptability, appropriateness, efficacy and sustainability. The analysis showed that the ESOGER tool was acceptable in its form and length, and both older adults and providers appreciated the use of the telephone as a means of administration for its familiarity and ease of use (acceptability). Both groups also agreed that the ESOGER provided useful information on mental and social needs, and not so much for physical needs as these were generally already well-known (appropriateness). Also, older adults found that the tool could help in preparing for their consultation, while healthcare providers noticed that it may help in setting care agendas and the general management of patients (efficacy). Finally, ESOGER appeared to be particularly appreciated by both older adults and healthcare provider when the information provided by the patient is discussed explicitly during the consultation (sustainability). Conclusion: Both groups saw benefits in using a preconsultation tool such as ESOGER, particularly in its use to assist in talking about mental and social need. Nevertheless, this tool could benefit from adaptations regarding the social and psychological needs of older adults and its use according to different clinic workflows.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".