Emerging health technology firms’ strategies and their impact on economic and healthcare system actors: a qualitative study
Bibliographic record
Abstract
A growing number of announcements on new and innovative medical devices are reported each year by economic actors. However, very few new technologies are successfully acquired and adopted by healthcare actors. To examine how economic and healthcare system actors perceive entrepreneurs’ strategies employed to respond to and address healthcare system actors’ pressures following firm’s emergence, we gathered data with 20 healthcare system and economic actors using semi-structured interviews and thematic analysis. We have determined that the acquisition and diffusion of health technologies are increasingly regulated and must respond to increasing pressures from many actors who see their agency power decline. We have found that political strategies address the pressures from institutionalization of practices and decoupling of the health system and its goals, associative strategies react to the power of key influencers such as investors and medical specialists, and mistrust of marketing actions, normative strategies respond to pressures stemming from the growing need for evidence-based data; finally, identity strategies answer to the fragmentation of a public health system and the heterogeneity of local procurement processes are approached. The results may help medical professionals, decision-makers, and evaluators to understand medical device acquisition and diffusion process better.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".