Access to multidisciplinary outpatient heart failure clinics in Qatar: a qualitative study from the perspectives of patients and cardiologists
Bibliographic record
Abstract
OBJECTIVE: Heart failure clinics (HFCs) are associated with increased survival rates, lower hospitalisation and improved quality of life. This study investigated factors influencing patient access to multidisciplinary outpatient HFCs from the perspective of patients and cardiologists. DESIGN: This was a qualitative study. A trained researcher conducted semistructured face-to-face interviews with patients and online interviews with cardiologists. Interviews, conducted between March and October 2023, were audio-recorded. Transcripts were cleaned (deidentification, translation verification) and analysed by two trained researchers independently using systematic text condensation in NVivo v12. Codes were derived from the transcripts and grouped and organised into themes. Two authors independently coded data, reconciling disagreements with the senior author, followed by respondent validation. Member checking ensued. SETTING: Outpatient multidisciplinary HFCs in Qatar. PARTICIPANTS: A purposive sample of patients diagnosed with heart failure who had attended at least one HFC appointment at Qatar's Heart Hospital were approached in person or via phone, and cardiologists with the authority to make referrals to these clinics via the electronic medical record system were emailed; interviews ensued until theme saturation was achieved. RESULTS: 26 individuals (14 patients and 12 cardiologists) participated in the interviews. Four major themes were identified: health system organisation (subthemes: benefits, HFC triage criteria, need/capacity), HFC referral processes (subthemes: electronic record system, patient communication and education), care continuity and communication (subthemes: patient navigators, clinician preferences) and access challenges (subthemes: transportation, costs). CONCLUSIONS: Resources are needed to expand HFC capacity and coverage, leverage electronic medical record tools as well as telehealth, educate physicians and patients on referral guidelines and processes and engage primary care to ultimately improve patient outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".