Perspectives from Geriatric In-patients with Heart Failure, and their Caregivers, on Gaps in Care Quality
Bibliographic record
Abstract
BACKGROUND: Evidence indicates that care experiences for complex HF patients could be improved by simple organizational and process changes, rather than complex clinical mechanisms. This survey identifies care gaps and recommends simple changes. METHODS: The study utilized both quantitative and qualitative methods at The Ottawa Hospital, Geriatric Medical Unit during a three-month period. RESULTS: Nineteen patients (average age 85, 12 female) surveyed. Twelve participants lived alone. Fourteen lived in own home. Four patients had formal home-care services. Fifteen relied on family. Gaps were identified in in-patient practice, discharge plan, and discharge summary implementation feedback. Only five participants had seen cardiologist or specialist. Half of patients did not know if they were on a special HF diet. Participants did not recall receiving information on life expectancy but were comfortable discussing EoL care and dying. HF-specific management recommendations were mentioned in only 37% of discharge summaries to PCPs. CONCLUSION: The results provide the starting point for a quality assurance and process re-engineering program in GMU. Organization change is needed to develop and integrate a cardiogeriatric clinical framework to allow the cardiologist, geriatrician, and PCP to actively work as a team with the patient/caregiver to develop the optimal care plan pre- and post-discharge.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| 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".