Electronic Clinical Records for Physiotherapists
Bibliographic record
Abstract
Purpose: This pilot study compared traditional (paper-based) and electronic (computerized) clinical physiotherapy records. The content of the records and the software’s user acceptability were considered. Methods: A neuro-musculoskeletal patient scenario involving two encounters (initial and follow-up) was scripted and role-played to each of three experienced physiotherapists (A, B and C). Participants assessed the patient and made traditional clinical records. After basic training in an electronic record system, they repeated the assessments and made electronic records via a laptop computer. Three experienced physiotherapists (A, D and E) each used their usual method to write a clinical report and an electronic record to write a report with the aid of the software’s report tool. The two participants who wrote reports but did not assess the patient (D and E) received a brief software demonstration just prior to writing the electronic record report. The electronic and traditional clinical records and reports were compared regarding their content and completion time. Participants recorded their expectations and experience of learning and using the electronic record system via questionnaires. Results: Participants expressed initial apprehension regarding an unfamiliar documentation system, but generally found the electronic system easy to learn and use. Some would have preferred additional customization options. All traditional records contained pages that lacked patient identification details. The electronic records contained more details related to symptoms, social circumstances and physical examination findings. The participants used more time for assessment and recording the initial examination when using the electronic system. Participants reported easier data retrieval from the computerized records than from the traditional records. Conclusions:The electronic clinical record system may prompt more complete recording and facilitate better patient record identification. These effects have implications for patient care, communication between providers and clinicians’ medico-legal protection. Further research is needed to determine the system’s efficiency and to clarify the impact of other characteristics of electronic record systems for physiotherapists.
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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 | 0.017 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".