Public health investigation of infection prevention and control complaints in Ontario, 2015–2018
Bibliographic record
Abstract
BACKGROUND: in 2015, Ontario public health units have been mandated to investigate infection prevention and control (IPAC) complaints in various settings, including those where regulated health professionals work. No surveillance system exists for IPAC complaints; therefore, little is known about their occurrence. Anecdotal evidence suggests a recent increase in IPAC complaints resulting in increased demand on public health resources. OBJECTIVES: To describe the occurrence of IPAC complaints and lapses in Ontario in 2015-2018 and the public health response to these. METHODS: Ontario public health units were surveyed about the occurrence and key challenges of IPAC complaint investigations through closed- and open-ended questions. The survey was disseminated through the Council of Ontario Medical Officers of Health listserv. Data collection spanned February 4-28, 2019. Descriptive statistical analyses and thematic analysis of free-text responses were performed. RESULTS: Twenty-one public health units responded for a 60% response rate; fewer responding health units had a population size of less than 100,000. A nearly six-fold increase in IPAC complaints was found, from a total of 79 complaints in 2015 to 451 in 2018. IPAC lapses nearly tripled, with 61 identified in 2015 and 168 in 2018. Whereas variation in the number of IPAC complaints and lapses among public health units was noted, the most common IPAC lapse involved inadequate reprocessing of reusable equipment. Key challenges in investigating IPAC complaints included lack of staff expertise/training, increased workload and costs, interjurisdictional inconsistencies and lack of guidance. CONCLUSION: IPAC complaints and lapses have increased in Ontario since 2015 when the Ministry of Health and Long-Term Care changed the IPAC complaint protocol. Public health units identified lack of expertise, increased workload, interjurisdictional inconsistencies and lack of guidance as challenges. Further research to confirm these findings, identify best practices to address these challenges as well as interventions to prevent IPAC lapses would be useful. Prospective surveillance of IPAC complaints, like for reportable diseases, would also be useful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".