Application of the i-PARIHS framework in the implementation of speech recognition technology as a way of addressing documentation burden within a mental health context.
Bibliographic record
Abstract
Documentation burden continues to be a critical issue in the adoption of comprehensive electronic health record systems. This case study demonstrates how the i-PARIHS framework can be applied to support the implementation of interventions in reducing documentation and EHR-related burden in a mental health context. As part of pre-adoption implementation activities for Speech Recognition Technology (SRT), a cross-sectional survey was conducted with physicians, residents, and fellows at an academic mental health hospital to explore their perceptions on SRT. Open-ended responses and follow-up interviews explored challenges and concerns on using SRT in practice. Through an analysis using the i-PARIHS framework, key considerations were mapped across the four components of the framework. This study demonstrates the value of applying well-established implementation frameworks, such as the i-PARIHS framework, in mitigating challenges related to documentation burden. Future studies should explore how implementation frameworks can be systematically embedded in addressing EHR-related burden.
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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 | 0.061 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| 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".