The care delivery experience of hospitalized patients with complex chronic disease
Bibliographic record
Abstract
OBJECTIVE: This study investigated what is important in care delivery from the perspective of hospital inpatients with complex chronic disease, a currently understudied population. PARTICIPANTS AND SETTING: One-on-one semi-structured interviews were conducted with inpatients at a continuing care/rehabilitation hospital (n = 116) in Canada between February and July 2011. DESIGN: The study design was mixed methods and reports on patient characteristics and care delivery experiences. Basic descriptive statistics were run using SPSS version 17, and thematic analysis on the transcripts was conducted using NVivo9 software. RESULTS: Patients had an average of 5 morbidities and several illness symptoms including activity of daily living impairments, physical pain and emotional disturbance. Three broad themes (each with one or more subthemes) were generated from the data representing important components of care delivery: components of the care plan (a comprehensive assessment, supported transitions and a bio-psycho-social care package); care capacity and quality (optimal staff to patient ratios, quicker response times, better patient-provider communication and consistency between providers) and the patient-provider relationships (characterized by respect and dignity). CONCLUSIONS: As health systems throughout the industrialized world move to sustain health budgets while optimizing quality of care, it is critical to better understand this population, so that appropriate metrics, services and policies can be developed. The study has generated a body of evidence on the important components of care delivery from the perspectives of a diverse group of chronically ill individuals who have spent a considerable amount of time in the health-care system. Moving forward, exploration around the appropriate funding models and skill mix is needed to move the evidence into changed practice.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 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".