Barriers for integrating personalized medicine into clinical practice: A qualitative analysis
Bibliographic record
Abstract
Personalized medicine-tailoring interventions based on individual's genetic information-will likely change routine clinical practice in the future. Yet, how practitioners plan to apply genetic information to inform medical decision making remains unclear. We aimed to investigate physician's perception about the future role of personalized medicine, and to identify the factors that influence their decision in using genetic testing in their practice. We conducted three semi-structured focus groups in three health regions (Fraser, Vancouver coastal, and Interior) in British Columbia, Canada. In the focus groups, participants discussed four topics on personalized medicine: (i) physicians' general understanding, (ii) advantages and disadvantages, (iii) potential impact and role in future clinical practice, and (iv) perceived barriers to integrating personalized medicine into clinical practice. Approximately 36% (n = 9) of physicians self-reported that they were not familiar with the concept of personalized medicine. After introducing the concept, the majority of physicians (68%, n = 19 of 28) were interested in incorporating personalized medicine in their practice, provided they have access to the necessary knowledge and tools. Participants mostly believed that genetic developments will directly affect their practice in the future. The key concerns highlighted were physician's access to clinical guidelines and training opportunities for the use of genetic testing and data interpretation. Despite the challenges that personalized medicine can create, in general, physicians in the focus groups expressed strong interest in using genetic information in their practice if they have access to the necessary knowledge and tools.
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 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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".