Assessment of the infection prevention and control learning needs of Ottawa community-based healthcare providers
Bibliographic record
Abstract
Background: Under the Health Protection and Promotion Act and Infection Prevention and Control (IPAC) Complaint Protocol, Ontario public health units are mandated to respond to IPAC complaints about community-based clinical offices. From 2015 to 2018, Ottawa Public Health noted a seven-fold increase in IPAC complaints involving medical and dental settings. In response, we sought to assess the IPAC learning needs of our community-based healthcare providers. Specifically, our objectives were to assess: 1) clinical practice characteristics, 2) current IPAC practices, 3) IPAC knowledge, 4) barriers/facilitators to adherence to IPAC best practices, and 5) preferred IPAC professional development activities. Methods: An anonymous online survey targeting Ottawa community-based healthcare providers was disseminated through multiple methods including through Ottawa Public Health’s (OPH) subscription-based e-bulletin to physicians. The short survey questionnaire included Likert-scale, multiple choice, and open-ended questions. Data collection began in August 2018; a descriptive analysis was conducted using data extracted on January 19, 2019. Results: Our findings suggest that medical respondents may not be as aware of IPAC practices in their clinic as dental respondents were. Familiarity with IPAC best practice documents was also higher among dental respondents, as compared to medical respondents. IPAC knowledge-testing questions revealed that more medical than dental respondents knew the appropriate use of multi-dose vials, and that few medical respondents knew the IPAC best practices for point-of-care glucose monitoring equipment. Respondents recognized the importance of adhering to IPAC best practices to prevent healthcare-associated infections; however, lack of evidence and cost were selfreported barriers to adherence to IPAC best practices. Over half of all medical and dental respondents surveyed were interested in a voluntary audit of their IPAC practices to help meet their IPAC professional development needs. Conclusions: Findings from this needs assessment helped describe current IPAC practices and knowledge, identify barriers and facilitators to adherence to IPAC best practices, and understand the learning preferences of Ottawa community-based healthcare providers. This information will be instrumental in planning future IPAC capacity-building activities and tailoring these activities to specific professional groups in Ottawa and potentially beyond.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".