Interrater Reliability in Content Analysis of Healthcare Service Quality Using Montreal’s Conceptual Framework
Bibliographic record
Abstract
Abstract: This study examines the usefulness of the Montreal Service Concept framework of service quality measurement, when it was used as a predefined set of codes in content analysis of patients’ responses. As well, the study quantifies the interrater agreement of coded data. Two raters independently reviewed each of the responses from a mail survey of ambulatory patients about the quality of care and recorded whether or not a patient expressed each concern. Interrater agreement was measured in three ways: the percent crude agreement, Cohen’s kappa, and the coefficient of the generalizability theory. We found all levels of interrater code-specific agreement to be over 96%. All kappa values were above 0.80, except four codes associated with rarely observed characteristics. A coefficient of generalizability equal to 0.93 was obtained. All indices consistently revealed substantial agreement. We empirically showed that the content categories of the Montreal Service Concept were exhaustive and reliable in a well-defined content-analysis procedure.
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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 | 0.183 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".