Improving safety culture in hospitals: Facilitators and barriers to implementation of Systemic Falls Investigative Method (SFIM)
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess the facilitators and barriers to implementation of the Systemic Falls Investigative Method (SFIM) on selected hospital units. DESIGN: A cross-sectional explanatory mixed methods design was used to converge results from a standardized safety culture survey with themes that emerged from interviews and focus groups. Findings were organized by six elements of the Ottawa Model of Research Use framework. SETTING: A geriatric rehabilitation unit of an acute care hospital and a neurological unit of a rehabilitation hospital were selected purposefully due to the high frequency of falls. PARTICIPANTS: Hospital staff who took part in: surveys (n = 39), interviews (n = 10) and focus groups (n = 12), and 38 people who were interviewed during falls investigations: fallers, family, unit staff and hospital management. INTERVENTION: Implementation of the SFIM to investigate fall occurrences. MAIN OUTCOME MEASURE(S): Percent of positive responses on the Modified Stanford Patient Safety Culture Survey Instrument converged with qualitative themes on facilitators and barriers for intervention implementation. RESULTS: Both hospital units had an overall poor safety culture which hindered intervention implementation. Facilitators were hospital accreditation, strong emphasis on patient safety, infrastructure and dedicated champions. Barriers included heavy workloads, lack of time, lack of resources and poor communication. CONCLUSIONS: Successful implementation of SFIM requires regulatory and organizational support, committed frontline staff and allocation of resources to identify active causes and latent contributing factors to falls. System-wide adjustments show promise for promotion of safety culture in hospitals where falls happen regularly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".