Process of the integration of patient advisors in oncology: Patient advisors' views.
Bibliographic record
Abstract
54 Background: Since 2017, in Quebec (Canada), a research project is focusing on the feasibility, the processes, and the contribution of integrating patients who have already had cancer experiences (named patient advisors (PA)) in the oncology clinical team. In fact, the trained PA meet patients in health care settings in addition to the other health professionals in order to improve health care and partnership between professionals and patients. This presentation communicates the PA’s perception of their contribution and the factors influencing implementation of these programs in 4 establishments. Methods: Between April and May 2020, semi-structured virtual interviews lasting about 60 minutes were conducted on 12 patient advisors involved since 2017 in the elaboration of these support programs. The interviews were recorded and transcribed completely. The data analysis was done with content analysis using the QDA Miner 5.0 software, applying the qualitative analysis quality standards. Results: First of all, the 12 PAs have reported that the accompaniment of 67 patients have allowed them to share their experiential knowledge not only related to living with the illness but also about healthcare organization and the anticipation of treatments. They also highlighted their capacity to share situations that can be improved by healthcare professionals (example: wait times for exams; patients’ difficulty in understanding their illness, etc). However, they also highlighted 4 factors which limit the implementation: 1) organizational (the unclear and changing nature of the breast cancer consultation schedule, difficulty of access to consultation rooms and unsuitable schedules for the PA’s reality.; 2) leadership (lack of clinicians promoting the intervention; managers’ low involvement; lack of clarity of roles); 3) human (work overload and employee turnover); 4) PA’s status (lack of compensation and of recognition). Also, to optimize their integration, the PA’s suggest to work on a lasting bond of trust with the professionals, the creation of a status recognized by health establishments, the recognition of their role by the clinical team, to give them access to physical spaces, and an openness of the teams to their complementary knowledge. Conclusions: These first results suggest the importance during the implementation of such programs of working proactively with the PAs and the professionals on organization, leadership, resources, and status factors to allow the people dealing with cancer to benefit from experiential knowledge of other patients within their clinical team.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".