Exploring the experiences of physiotherapists who engaged as knowledge users in integrated knowledge translation research partnerships related to balance measurement practices in Canadian hospitals: a qualitative descriptive study
Bibliographic record
Abstract
Background: Integrated knowledge translation (IKT) is an approach to doing health research that engages academic researchers and knowledge users (KU) as equal partners. IKT intends to increase the chances that resulting research evidence will be useful to those engaged, striving toward improved health system functioning and public health outcomes. With this study, I set out to learn what physiotherapists (PTs) had to say about their experience engaging as KUs in an IKT research partnership related to balance measurement practices in Canadian hospitals. Methods: I used basic qualitative descriptive research methodology, in vivo coding, and conventional content analysis to answer the research questions. Five PTs (n=5) who had engaged as KUs on three balance measurement studies in two provinces were purposefully selected. All five (n=5) participated in online semi-structured interviews. PTs were asked to describe their IKT engagement experience, identify environmental factors that affected their engagement, and discuss how their engagement influenced the research process and evidence use. PTs also characterized themselves using an independently completed pre-interview questionnaire. Results: Participants described their experiences as positive, meaningful, and associated with benefits such as more clinical treatment options, greater sense of personal pride and professional recognition among PTs, increased research capacity for host organizations, and specific contributions to a body of knowledge. PTs said factors conducive to IKT engagement were supportive organizational culture, as well as devoted time, money, material resources, and human resources. PTs described their contributions to research as brokering trusting relationships; providing an insider point-of-view, project management, and resource coordination; and contributing to increased organizational capacity for research. Participants described how evidence-use was impacted by PT career-stage, individual risk perception, usefulness to the profession, organizational culture, treatment environment (especially since COVID-19 introduced pressures to deliver health care online), and third-party endorsement for change. Conclusions: KU engagement in IKT health research partnerships provides researchers with increased clinical access, an insider point-of-view, and stronger research evidence. KU engagement increases the accessibility of resulting research evidence, but sustaining desired outcomes is another issue. The KU engagement experience is greatly affected by organizational culture. KU engagement concepts in IKT research partnerships must include feasibility and resource planning, as well as strategies for organizational change and risk management. PTs described external factors such as professional endorsement as being stronger influences on evidence use outcomes than research engagement. The IKT approach may be strengthened if issues related to change, risk, and resources are addressed early and often throughout the partnership.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".