Physician and nurse practitioner perspectives of a modified Routine Opioid Outcome Monitoring (ROOM) Tool
Bibliographic record
Abstract
Introduction The Routine Opioid Outcome Monitoring (ROOM) Tool was developed for use in community pharmacies in Australia. It facilitates pharmacists' screening and brief interventions regarding an individual's opioid use for chronic pain. At our academic teaching hospital, the ROOM Tool was adapted to incorporate a communication tool that includes a pharmacist's assessment and recommendations for primary care providers. This modified ROOM Tool was implemented as part of usual care in our outpatient pharmacies; however, the value to primary care providers is unknown. Aim The aim of this study was to determine primary care provider perspectives on the modified ROOM Tool. Methods Focus groups were conducted with primary care providers from an Academic Family Health Team. The focus group encompassed topics related to the positive and negative aspects of the modified ROOM Tool in supporting the care of patients using opioids for chronic pain. Qualitative content analysis of transcripts was performed to identify themes. Results Three focus groups were conducted with a total of six participants. Four themes emerged: (i) Facilitators to using the tool, (ii) Barriers to using the tool, (iii) Recommendations for improvement, (iv) Impact of the tool on patient care and safety. Discussion The ROOM Tool paired with the communication tool supports collaboration between pharmacists and primary care providers. The communication tool standardises the approach for communicating the pharmacist's assessment and recommendations. Recommendations to refine this modified ROOM Tool may increase its utility to primary care providers and enhance the impact on patient care and safety.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".