Use of patient-reported outcome measures (PROMs) in primary care-based mental health programming: an environmental scan of Alberta, Canada
Bibliographic record
Abstract
Many Primary Care Networks (PCNs) in Alberta collect Patient Reported Outcome Measures (PROMs) to support patient-centered care. However, there is limited knowledge on what tools are currently being administered across PCNs and how the data is used. For this study, we focused on PROMs for mental health programming (MHP). Our objectives are to identify what PROMs are currently being administered in PCNs and what domains they measure for MHP; understand PCNs’ capacity to implement and use PROMs data effectively for their PCN MHP; describe how PROMs are currently being reported in PCNs for MHP; and understand the feasibility of having standardized and consistent measurement of PROMs in general across PCNs. This environmental scan employs a survey for PCN evaluators (those responsible for managing PROMs data for their PCN), tailored to examine PROMs in PCN MHP across all populations. Evaluators from all 39 Alberta PCNs were invited to complete the survey on behalf of their PCN. It included closed and open-ended questions. Survey results were aggregated and reported by objective. Evaluators from 20 PCNs (51%) completed the survey, with a mix of rural/urban and across all five health zones. Nine out of 20 reported 11 tools currently being collected and seven out of nine reported using more than one tool for MHP. The most used tools were the EQ-5D-5 L (7/9) and PHQ-9 (6/9). Seven respondents indicated the EQ-5D-5Lwas useful or sometimes useful; five reported the PHQ-9 was useful or sometimes useful. While the use of each PROM varied, most PROMs are used for clinical care decisions and internal reporting. Most respondents indicated standardizing PROMs across PCNs would be challenging, however having alignment of PROMs and sharing best practices for PCNs would be beneficial. These results provide a better understanding of the current use of PROMs in PCNs, specific to MHP, which will be further examined through future narrative conversations. Overall, this study informs primary care leadership on the current use of PROMs and supports the advancement of PROMs use in Alberta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".