Practising family medicine for adults with intellectual disabilities: patient perspectives on helpful interactions.
Bibliographic record
Abstract
OBJECTIVE: To explore the perspectives of adults with intellectual disabilities (IDs) on helpful interactions with their family physicians. DESIGN: Exploratory, qualitative study. SETTING: Vancouver, BC. PARTICIPANTS: Purposive sample of 11 community-dwelling adults with IDs. METHODS: In-depth, semistructured interviews were conducted face to face with participants. Interviews were audiorecorded and transcribed verbatim. Research team members read the transcripts, which were then coded into categories and subcategories and discussed at collective analysis meetings. The main study themes were generated through this iterative, collective process. MAIN FINDINGS: Two themes about helpful interactions were identified: helping patients understand and helping patients navigate the health care system. The first theme reflected helpful ways of communicating with patients with IDs. These approaches focused on plain-language communication and other strategies developed jointly by the patients and their physicians. The second theme reflected ways in which the family physicians helped adults with IDs manage their health needs despite the complex constraints of their socioeconomic situations. CONCLUSION: Adults with IDs want to play an active role in managing their health as they age, and helpful interactions with family physicians make this possible.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".