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Implementation of a competency framework in an infection prevention and control program: An evaluation using the RE-AIM framework

2025· article· en· W4411407817 on OpenAlexvenueaboutno aff
Kathryn Bush, Christian Tsang, Blanda Chow, Obeleye Tamuno, Heather Gagnon

Bibliographic record

VenueCanadian Journal of Infection Control · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationHealth careMedical educationMedicineKnowledge managementNursingPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Background: The role of infection control professionals has evolved with the increasing complexity of healthcare and the diversity of professional backgrounds, including nursing, epidemiology, and public health. This diversity presents challenges for orientation and professional development. Recognizing the importance of competency-based frameworks, the Alberta Health Services Provincial Infection Prevention and Control (IPAC) program implemented a strategic initiative to define and operationalize competencies across IPAC roles. This study evaluated the initiative using the RE-AIM framework. Methods: The IPAC competency framework was launched in March 2022. A multi-method evaluation was conducted between January and March 2024, consisting of an anonymous survey distributed to 217 IPAC staff and follow-up interviews with senior leadership. The RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) guided the evaluation. Results: Sixty percent of staff (130/217) completed the survey. Under the Reach domain, most staff reported using and understanding the competencies, though some found prioritization challenging. Effectiveness was reflected in 79% of staff setting learning goals and 88% developing actionable plans. Adoption showed high satisfaction, with 88% of respondents using competency tools, though some reported perceived redundancy. Conclusions: Factors influencing the use of the IPAC competency tools included alignment with program goals, leadership engagement, delegation of champions, and barriers to staff engagement. Ongoing evaluation was recommended to monitor progress and enhance sustainability. The RE-AIM framework proved useful in identifying both successes and potential risks to the long-term success of the competency framework. Key barriers included limited team size and competing priorities; however, continued training and perceived value were identified as critical to sustained engagement.

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 imitation

Not 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.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.504
Teacher spread0.461 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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