Exploring the perspectives of outpatient rehabilitation clinicians on the challenges with monitoring patient health, function and activity in the community
Bibliographic record
Abstract
PURPOSE: Rehabilitation clinicians need information about patient activities in the home/community to inform care. Despite active efforts to develop technologies that can meet this need, clinicians' perspectives regarding how information is collected and used in outpatient rehabilitation have not been comprehensively described. Therefore, we aimed to describe: (1) what data pertaining to a patient's health, function and activity in their home/community are currently collected in outpatient rehabilitation, (2) how these data can impact clinical decisions, and (3) what challenges clinicians encounter when they manage the care of outpatients based on this information. MATERIALS AND METHODS: Eight clinicians working in outpatient rehabilitation programs completed qualitative interviews that were analyzed using an inductive thematic analysis. RESULTS: Four themes were identified: "Nature of data about a patient's health, function and activity in the home/community and how it is collected by clinicians," "Value of data from the home/community," "Perceived drawbacks of current data collection methods," and "Improving data collection to understand patient trajectory." CONCLUSIONS: Clinicians described the importance of understanding patient activities in the home/community, but perspectives varied regarding the suitability of current methods. These perceptions may inform the design of solutions to bridge the gap between the clinic and the community in outpatient rehabilitation.Implications for rehabilitationClinical decision-making in outpatient rehabilitation is guided by verbal and written reports about a patient's health and function in the community and adherence to treatment plans.Differing perceptions on the suitability of current data collection methods indicate that the development of new solutions, such as rehabilitation technologies, needs to carefully consider clinician workflows and what data are perceived as meaningful.Potentially impactful directions for new solutions include providing well validated data on adherence, movement quality, or longitudinal progression, presented in formats that match clinical decision criteria.
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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 | 0.044 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| 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".