Value Co-Creation through Patient Engagement in Health Care: A Bibliometric Analysis
Bibliographic record
Abstract
This bibliometric study aims to provide an updated analysis of the global research conducted on value co-creation in healthcare.The methodology involved a comprehensive search of the Dimensions and PubMed databases to retrieve peer-reviewed publications up to March 2024.The search terms used were related to value co-creation, patient engagement, shared decision-making, and other relevant topics.A total of 1,588 relevant publications were identified and analysed.Service-dominant logic was used as the theoretical framework to examine value co-creation.The results show a significant rise in scholarly output since 2010, indicating growing research interest in value co-creation.After analysing the results, several countries stand out as the most active, including the United States, United Kingdom, Canada, and Australia.Hence, universities like the university of Toronto are weighed very high in terms of productivity.Journals like the Journal of Service Research and Journal of Medical Internet Research feature prominently.Authors like Janet R. McColl-Kennedy receive high citations.All in all, the study gives an inferred result comprising the tendencies of the research in the specified subject area, influential contributors, and their articles, and identifying the significant advancements in the field.Although, some limitations consist in possible biases and missing works that are not published or written in languages other than English.In conclusion, the bibliometric analysis provides an all-round vision of the emerging research frontiers to contribute to the understanding of what types of research still require more attention on the subject of value co-creation in healthcare.
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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 | 0.022 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.183 | 0.299 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".