Cognitive flow in health care settings: A systematic review
Bibliographic record
Abstract
INTRODUCTION: The state of cognitive flow, colloquially known as being 'in the zone', has been linked with enhanced performance, happiness, career satisfaction and decreased burnout. However, the concept has not been adopted strongly in health care training, continuing professional development, or daily practice. A systematic review with a narrative synthesis was undertaken to map the evidence for flow in health care. METHODS: A search was conducted in MEDLINE, PsycInfo, and EMBASE in July 2019 and updated in October 2020 for manuscripts discussing flow in all health care disciplines. Articles published between 1806 and 13 October 2020 were included. Two authors independently reviewed titles and abstracts (and subsequently full texts where necessary) for inclusion. Disagreements were resolved by consensus. Data were extracted on location, manuscript type, population and context, measures, and key findings. RESULTS: A total of 4923 unique abstracts were initially retrieved, and 15 articles were included in the final review. We report on the experience, benefits and strategies to support flow in health care. Flow may benefit providers by enhancing career enjoyment, wellness and performance, while mitigating exhaustion, burnout, and stress. Although research from other domains has focused on supporting flow through individualised training, our results highlight the importance of system and environmental factors. CONCLUSIONS: Supporting professional and trainee flow in health care requires a holistic approach, including individual training and system-level interventions.
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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".