“I Think It, but Don't Often Write It”: The Barriers to Charting in Private Practice
Bibliographic record
Abstract
PURPOSE: To describe barriers to charting identified by physiotherapists working in private practice in New Brunswick. METHOD: Physiotherapists were invited to focus-group interviews to discuss the results of a comprehensive chart audit. Sixty-nine physiotherapists who responded were assigned to nine focus groups. Seven of nine audiotaped interviews (49 participants) were of sufficient quality to be transcribed and imported into qualitative data analysis software for thematic analysis. RESULTS: Participants described the challenges of including charting in their routine client care. Barriers included the disjuncture between charting and thinking, the translation of impairment goals to functional goals, the time it takes to chart, fear of failure, and the difficulty of predicting length of treatment. Strategies to facilitate charting were suggested by participants. CONCLUSION: Understanding barriers to charting in private practice is necessary to improve the quality of documentation. Barriers described are related to the fast-moving nonverbal, kinaesthetic, and cognitive process that is clinical reasoning in physiotherapy. This tacit, implicit process is mismatched with the charting task, which requires that the implicit become explicit in written form. Strategies to facilitate charting noted by participants address some of these issues; however, a broader, profession-wide discussion is necessary.
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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 | 0.021 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".