Bibliographic record
Abstract
Abstract Background Leadership and chronic diseases are two frequent topics in current public health discussions. We performed a bibliometric analysis to study what interactions exist between these two themes. This study allows an insight on what is being published and also identifies potential gaps that need to be addressed. Methods We have reviewed all the titles and abstracts of articles available at PUBMED with the MESH terms ’Leadership’ and ’Chronic Disease’. We collected information about the authors, year, journal and type of publication and country where the study was done. We have also done a qualitative analysis on the themes addressed. Results We have found 171 entries, of witch 85 (49,7%) referred to apparent peer reviewed studies in English. All other publications referred to editorials, commentaries or the PUBMED entry did not allow for greater clarification. From 1998 the publication of articles became regular, with a peak of 14 articles published in 2014. The average of authors per publication was 3,19. The most frequent country involved was the USA (62), follow by the UK (14), Canada (11) and Australia (10). 128 publications were identified, of which BMC Health Serv Res was the one with the highest number of articles included (5). There was some form of a call for greater leadership from nurses in 22 articles. We also highlight 5 articles that called for a greater role of pastors and religious communities in this field. Conclusions Despite the perception of being common topics, there is still a low rate of publication of studies in the field of leadership and chronic diseases. There is a predominance of articles from the USA. There was not a predominant publication in this field. Despite doctors being typically seen as the leaders within the health field, the articles included seem to point to a trend in calling for a bigger leadership role of other actors, such as nurses. Key messages There is a need for more research in the field of leadership in chronic diseases. There seems to be a trend calling for greater leadership in the field from non-physician actors.
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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 | 0.033 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.248 | 0.313 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".