MétaCan
Menu
← Back to cohort
Record W4388719974 · doi:10.1370/afm.22.s1.4657

Engage, Discover, Change: Putting a Research Strategic Plan into Action

2023· article· en· W4388719974 on OpenAlexaboutno aff
Olga Szafran, Shannon Gentilini, Donna Manca, Kimberley Duerksen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationStrategic planningParticipatory action researchAction researchAction planKnowledge managementContext (archaeology)Public relationsMedical educationBusinessMedicinePsychologyPolitical scienceSociologyManagementComputer scienceMarketingPedagogy

Abstract

fetched live from OpenAlex

CONTEXT: Our Department of Family Medicine (DoFM) completed a research strategic planning process in 2019. Four research strategic goals were identified, to: build research capacity; demonstrate impact; make research meaningful to the practice of family medicine; and build meaningful engagement. Arising from these goals, eight strategic objectives were developed. An operational plan was needed to put the strategic objectives into action. OBJECTIVE: To develop a plan to operationalize the research strategic objectives. DESIGN: Participatory action approach. PARTICIPANTS/ SETTING: Faculty and staff in the DoFM, University of Alberta. INTERVENTION/ INSTRUMENT: From January 2020 to March 2021, the DOFM implemented an initiative to facilitate engagement necessary to operationalize the vision, mission, goals and objectives of the research strategic plan. A Research Leadership Team was formed and a Special Projects Coordinator was hired to guide the process. The process involved the establishment of five Working Groups: Patient-Centered Medical Home (PCMH); Research Engagement; Research Knowledge & Skills; Foster a Research Culture; and Work Smarter. Working Groups met to develop a set of actions for the respective strategic objectives. OUTCOMES/RESULTS: The main deliverables of the Working Groups included: PCMH - identified the need to further develop the data infrastructure to better inform practice, including data resources for research and quality improvement; Research Engagement - identified opportunities to engage stakeholders, residents, patients, and community family physicians in research; Research Knowledge & Skills - prioritized staff professional development training to build capacity and strengthen the core skills needed for research/scholarship; Foster a Research Culture - identified opportunities to build collegiality and share ideas that foster research curiosity; Work Smarter - implemented a department-wide survey to identify efficiencies and streamline processes, and established a Finance Working Group and a HR Working Group to streamline operations. CONCLUSION: A strategic plan requires an action plan to operationalize the strategic objectives. A leadership team and a coordinator are essential to put the plan into action. The process strengthened the research agenda and cultivated a culture of collaboration and engagement. While the COVID-19 pandemic temporarily disrupted momentum, the plan remains robust and relevant to continue forward.

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)0.220
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.780
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2200.128
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.004
Science and technology studies0.0220.037
Scholarly communication0.0310.026
Open science0.0070.036
Research integrity0.0160.027
Insufficient payload (model declined to judge)0.0090.005

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 head0.876
GPT teacher head0.635
Teacher spread0.241 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicHealth and Medical Research Impacts→French-language works237,207→