The <i>International Journal of Healthcare Management</i> ( <i>IJHM</i> ): Ten years after rebranding, a bibliometric analysis, and recommendations for improvement
Bibliographic record
Abstract
The ‘International Journal of Healthcare Management’ replaced the Journal of Management and Marketing in Healthcare since 2012. It marked its 10th anniversary in 2022 since its rebranding. This study undertakes the first bibliometrics analysis of the past 10 years of the International Journal of Healthcare Management covering the 2012–2022 period. Based on metadata of 627 articles published in the journal during this period, this article evaluates the intellectual, contextual, conceptual, and trend of the IJHM. The study's results were exciting in numerous ways: first, there is a steady increase in the number of publications published and cited in the literature. Second, a strong collaborative relationship with key sources in the IJHM was discovered. Third, the conceptual analysis that finds the theme trends of articles in the IJHM using the co-occurrence of keywords as the unit of analysis. The social structures, which concentrated on cooperation and the dynamics and effects of literary contributions, provide insights into future directions for sustaining or planning a worldwide focus for the Journal. Finally, several inexhaustive improvements have been suggested.
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 | 0.024 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.061 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".