What matters most to patients when they assess quality of their care?
Bibliographic record
Abstract
Objectives: To report the capabilities of a patient satisfaction questionnaire in capturing factors which are important to patients in their evaluations of the quality of care provided to them. Design: An experienced research officer introduced the study to all patients with defined tracer conditions in the Saskatoon Health Region from Jan to April of 2009. Patients who agreed to participate returned their completed questionnaire directly to the research officer or placed them in a special box held by the nursing unit clerk on their unit. Measures: The instrument contained: 18 items of the General Practice Assessment Questionnaire for physicians and nurses; as well as single items capturing patient observations regarding: attentiveness of nurses; tidiness of facilities; efficiency of tests and treatments; patient comments; and a grading scale assessing overall quality of care. Contextual items covered health status, expenses, insurance and demographics. A provider care model and a client satisfaction model were constructed and tested. Results: Almost 96 percent of eligible patients (n=378) completed the questionnaire. The provider care model explained 84.2 percent of the variation in patients’ assessments of overall quality; and the client satisfaction model explained 67.6 percent of the variation. The quality of nursing and medical care were, the most important factors; however, attentiveness, tidiness, efficiency, and quantified comments each explained small but significant percentages of variance in overall quality. Conclusions: Patients consider separate dimensions in their assessments of overall quality of care. While quality of care by professionals trumps other considerations, the passive role for patients is fading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".