Interprofessional collaborative practice within cancer teams: Translating evidence into action. A mixed methods study protocol
Bibliographic record
Abstract
BACKGROUND: A regional integrated cancer network has implemented a program (educational workshops, reflective and mentoring activities) designed to support the uptake of evidence-informed interprofessional collaborative practices (referred to in this text as EIPCP) within cancer teams. This research project, which relates to the Registered Nurses' Association of Ontario (RNAO) Best Practice Guidelines and other sources of research evidence, represents a unique opportunity to learn more about the factors and processes involved in the translation of evidence-based recommendations into professional practices. The planned study seeks to address context-specific challenges and the concerns of nurses and other stakeholders regarding the uptake of evidence-based recommendations to effectively promote and support interprofessional collaborative practices. AIM: This study aims to examine the uptake of evidence-based recommendations from best practice guidelines intended to enhance interprofessional collaborative practices within cancer teams. DESIGN: The planned study constitutes a practical trial, defined as a trial designed to provide comprehensive information that is grounded in real-world healthcare dynamics. An exploratory mixed methods study design will be used. It will involve collecting quantitative data to assess professionals' knowledge and attitudes, as well as practice environment factors associated with effective uptake of evidence-based recommendations. Semi-structured interviews will be conducted concurrently with care providers to gather qualitative data for describing the processes involved in the translation of evidence into action from both the users' (n = 12) and providers' (n = 24) perspectives. The Graham et al. Ottawa Model of Research Use will serve to construct operational definitions of concepts, and to establish the initial coding labels to be used in the thematic analysis of the qualitative data. Quantitative and qualitative results will be merged during interpretation to provide complementary perspectives of interrelated contextual factors that enhance the uptake of EIPCP and changes in professional practices. DISCUSSION: The information obtained from the study will produce new knowledge on the interventions and sources of support most conducive to the uptake of evidence and building of capacity to sustain new interprofessional collaborative practice patterns. It will provide new information on strategies for overcoming barriers to evidence-informed interventions. The findings will also pinpoint critical determinants of 'what works and why' taking into account the interplay between evidence, operational, relational micro-processes of care, uniqueness of patients' needs and preferences, and the local context.
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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 | 0.184 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".