Perceptions and Attitudes Regarding Medical Device Development in Canada Among Canadian Innovators: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVES: The Canadian medical device industry presents unique challenges to innovators. However, little attention has been paid to exploring the distinct experience of Canadian medical device innovators in the literature. The objective of this study is to explore the experience of Canadian innovators in navigating this industry, with a focus on their perceptions and attitudes towards the use of health economic evaluation. METHODS: Semi-structured interviews were conducted using virtual conferencing technology. All participants were C-level employees of small- and medium-sized enterprises (SMEs) with adequate knowledge of their company's overall strategy. Qualitative data were analyzed to reveal emerging themes. RESULTS: Interviews were performed with ten participants. Forty percent of participants rated themselves as having either minimal or basic knowledge of health economics. Thirty percent of participants had not pursued early economic evaluation of their device, while 90% rated health economics as being either "Quite important" or "Very important" to their company. The perception of increased barriers to successful device adoption in Canada relative to the USA was a prominent sentiment among participants, with 50% expressing discontentment with either the device approval process or health technology assessment process in Canada. Twenty percent stated that their primary target market involved the USA and/or other international jurisdictions. CONCLUSION: Canadian medical device innovators appear to understand the importance of health economic evaluation in the innovation process. However, they report difficulty with device approval and adoption, with some innovators focusing their efforts outside of Canada altogether. Further research should be directed toward understanding how to better support SMEs, given that they are a tremendous source of growth for the Canadian medical device industry.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".