Understanding the training, mentorship, and professional development priorities of early career embedded researchers
Bibliographic record
Abstract
INTRODUCTION: Health systems are constantly evolving in response to existing and emerging health challenges and are increasingly adopting the Quintuple Aim to guide transformation and improvement efforts. Addressing health challenges and achieving the Quintuple Aim (enhancing patient experience, improving healthcare provider experience, promoting population health, optimising the value of healthcare services, and advancing health equity) may be enhanced with the use of a Learning Health Systems approach that fosters the real-time use of data and evidence to inform improvement efforts and harnesses embedded researchers to co-produce timely, relevant evidence to address priorities. Training programs have emerged to build embedded research capacity within health system organisations and have focused predominantly on the postdoctoral career stage, with little attention paid to the early career researcher (ECR) stage. The objective of this study was to understand ECR training and mentorship needs in the embedded research context to inform the creation new or adaptation of existing programs to build embedded ECR capacity. METHODS: This study used a qualitative approach to garner insight from embedded and applied scholars and health systems leaders in Canada from various professional backgrounds and at various career stages using a combination of focus group discussions, key informant interviews, and an online survey. Thematic content analysis was used to examine the responses of study participants within the interview themes. RESULTS: Twenty-six (26) participants were included in the study. Results were organised according to four key themes: (1) key competencies and skills needed by embedded ECRs; (2) additional training and capacity development needs; (3) training delivery approaches; and (4) enablers and challenges faced by embedded ECRs. Results highlight the importance of supporting ECRs to develop their leadership and organisational management capabilities; their knowledge of and ability to use research approaches that are well-suited to real-world, complex, evolving environments; and their opportunities to learn with and from each other and mentors. Results underscore the perceived importance of context, including being embedded in a supportive environment that values research and evidence and of academic incentives that recognise and value real-world research impact. The challenges of responding to shifting organisational and system priorities were identified. Additional insights from health systems leaders were also highlighted. CONCLUSION: This study identified the multifaceted needs of embedded ECRs and the challenges they face within healthcare systems. Designing new programs or tailoring existing ones to address these needs would build their capacity, foster career progression, and ensure their impact as leaders of evidence-informed health system improvement which is crucial for achieving the Quintuple Aim.
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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 | 0.061 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".