eConsult Specialist Quality of Response (eSQUARE): A novel tool to measure specialist correspondence via electronic consultation
Bibliographic record
Abstract
High-quality correspondence between healthcare providers is critical for effective patient care. We developed an assessment tool to measure the quality of specialist correspondence to primary care providers (PCPs) via electronic consultation (eConsult), where specialists provide advice without specialist-patient interactions. We incorporated fourteen previously described features of high-quality eConsult correspondence into an assessment tool named the eConsult Specialist Quality of Response (eSQUARE). Six PCPs and two specialists applied the 10-item eSQUARE tool to 30 eConsults of varying quality as informed by PCP survey data. Content, response process, and internal structure validity evidence was gathered. Psychometric properties were calculated using descriptive statistics and generalizability analyses. Mean total score for low-quality eConsults (M = 24 ± 5.6) was significantly lower than moderate-quality eConsults (M = 38 ± 4.7; p<0.001) which was significantly lower than high-quality eConsults (M = 46 ± 3.0; p = 0.002). Reliability measures were high, including generalizability coefficient (0.96), inter-item (≥0.55) and item-total correlations (≥0.68). A decision study demonstrated that a single rater was adequate to achieve a reliability measure of ≥0.70. This study demonstrates initial validity evidence including multiple reliability measures for the eSQUARE. A single rater is adequate to achieve reliability measures for formative feedback. Future studies can apply the eSQUARE when planning educational initiatives aiming to improve specialist-to-PCP correspondence via eConsult.
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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 | 0.027 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".