Promoting the joy in academic medicine: A scoping review
Bibliographic record
Abstract
As academic medical leaders, we aimed to improve the workplace by promoting joy at work. Unlike deficit-based approaches that focus on burnout or disengagement, joy is a strength-based approach. Nurturing joy increases productivity, creativity, and happiness. To achieve our aim, we performed a scoping review on how leaders can better support joy at work for individuals in the academic medical setting. We searched seven databases, including peer-reviewed studies, books, book chapters, conference abstracts, and dissertations with no restriction on study design or country. Initial screen was abstract and title. Two reviewers screened, two extracted information, and a third reviewed entries. Discrepancies were resolved by consensus. 4649 publications were found (2465 after duplicate removal), 123 had full-text review, 25 met the inclusion criteria and were published between 1997 and 2023, conducted in the United States (n = 22), the United Kingdom (n = 2), and Canada (n = 1). Themes included shifting to a strengths-based focus on joy at work, implementing programs to prioritize it, and the key role of leaders in championing joy. Making system-level changes and adopting evidence-based programs that promote joy at work for academic physicians is effective. Ensuring that leaders are competent in using evidence-based approaches to improve joy is key.
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 | 0.030 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".