Exploration of Patient Safety Phenomena in Rehabilitation and Complex Continuing Care
Bibliographic record
Abstract
Patient safety has been relatively unexplored in rehabilitation and complex continuing care (CCC) settings. From the perspectives of staff members, this qualitative study aimed to explore patient safety phenomena that exist within rehabilitation/CCC and to identify the characteristics of the current workplace culture that act as enablers of or barriers to patient safety. Sixty-six staff members in a large, multisite, academic rehabilitation/CCC health centre volunteered to participate in one of six interprofessional focus groups, designed to model patient care teams that exist within the clinical programs; one focus group was also conducted with support services staff. Thematic analysis revealed that rehabilitation/CCC settings present with distinct patient safety issues due to the unique and increasingly complex populations that are served, and the place of rehabilitation/CCC along the continuum of care. Enablers and barriers identified related to teamwork, culture, resources and organizational and individual responsibility. Results of this study have helped form the foundation for future patient safety initiatives within our settings, with clear emphasis on enhancing an open and just culture in which to discuss safety issues through development of improved leadership-staff relations, teamwork and communication and clearer processes and structures for accountability. The approach to addressing these issues must fit within our rehabilitation models of care.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".