Exploring Health Care Professionals' Perceptions of Incidents and Incident Reporting in Rehabilitation Settings
Bibliographic record
Abstract
OBJECTIVES: Research exploring patient safety in rehabilitation settings is limited. This study's aim was to describe team members' perceptions of incidents and incident reporting in rehabilitation settings. METHODS: Semistructured interviews were conducted with 18 health care professionals from multiple rehabilitation units (medical, neurological, and orthopedic) at 2 inner-city rehabilitation centers. Five hypothetical scenarios were presented to participants during the interviews. Participants were asked to classify the scenarios and whether they would report any identified incidents. Data were analyzed using a descriptive thematic approach. RESULTS: Participants classified events based on 2 parameters: the nature of the outcome and deviation from professional practice. Factors influencing participants' decisions to file incident reports included their classification of the events in the scenarios (i.e., events classified as critical incidents were more often reported than those classified as incident or near miss); the severity of the impact on the client; and their profession's perceived role in reporting specific incidents. When participants said they would report incidents, all agreed that they would report only objective facts. CONCLUSIONS: The study findings demonstrate gaps between incident-reporting policy and practice, and opportunities to address these gaps. Organizational leaders can work with all health care professions to support their roles in reporting. Interprofessional team building, focused on valuing all team members, may improve interprofessional communication and reporting. Setting standards for classifying events could increase consistency in reporting. Ultimately, encouraging reporting of near misses and incidents can create a culture of learning focused on problem solving and improved patient 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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".