Knowledge to action: Needs assessment to enhance support for infection control professionals across healthcare settings
Bibliographic record
Abstract
Background: The purpose of this study was to describe needs of infection prevention and control (IPAC) professionals across healthcare settings in one region of Ontario, Canada to inform priorities for the development of resources and capacity-building activities. Methods: An open survey targeting IPAC stakeholders working in diverse healthcare settings was disseminated through multiple methods, including Public Health Ontario’s stakeholder management database. The survey was open from April 1 to June 30, 2019. IPAC inquiries documented by Public Health Ontario (PHO) staff between April 1, 2017 and March 31, 2019 were analyzed. The data collection tools and descriptive analysis were guided by the Knowledge to Action Cycle to identify gaps, understand barriers and opportunities, and preferred strategies for learning. Results: The survey was completed by 135 IPAC stakeholders with 56% of respondents working in long-term care and retirement homes, 13% in hospitals, 11% in primary care, 10% in home care and 10% in other settings. Respondents reported that there is a need for more support to improve practices around environmental cleaning, surveillance and routine practices; however, findings varied by setting. An important theme focused on the need for strategies to inspire and motivate stakeholders to invest in infection prevention and control. A lack of support in this area was a top barrier selected by 40% of survey respondents, and 67% expressed interest in skill development in this area. While communities of practice, common-interest networking groups, are frequently facilitated by PHO to support stakeholders, this strategy was not preferred by respondents. Conclusions: Future efforts to enhance support for IPAC can draw on these findings to help prioritize topics, understand barriers, and align with preferred methods for capacity building.
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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 | 0.043 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".