L’accessibilité aux soins : Perceptions des patients avec plusieurs problèmes de santé chroniques
Bibliographic record
Abstract
OBJECTIVE To explore access to health care for patients presenting with multiple chronic conditions and to identify barriers and factors conducive to access. \n \nDESIGN Qualitative study with focus groups. \n \nSETTING Family practice unit in Chicoutimi (Saguenay), Que. \n \nPARTICIPANTS Twenty-five male and female adult patients with at least four chronic conditions but no cognitive disorders or decompensating conditions. \n \nMETHODS For this pilot study, only three focus group discussions were held. \n \nMAIN FINDINGS The main barriers to accessing follow-up appointments included long waits on the telephone, automated telephone-answering systems, and needing to attend at specific times to obtain appointments. The main barriers to specialized care were long waiting times and the need to get prescriptions and referrals from family physicians. Factors reported conducive to access included systematic callbacks and the personal involvement of family physicians. Good communication between family physicians and specialists was also perceived to be an important factor in access. \n \nCONCLUSION Systematic callbacks, family physicians' personal efforts to obtain follow-up visits, and better physician-specialist communication were all suggested as ways to improve access to care for patients with multiple chronic conditions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".