H-index and Academic Medicine
Notice bibliographique
Résumé
I read with great interest the recent minireview by Schreiber and Giustini1 on the h-index. They need to be lauded for bringing this important issue concerning academic medicine to the forefront of this journal, which is read by many pathologists. However, the issue of the h-index is most germane to academic pathologists. As a senior semiretired professor who attained the rank of Distinguished Professor, I was always surprised at how few faculty were aware of the h-index and its value to those involved in promotion and tenure. With a career spanning 42 years in academic medicine and called on frequently to be a reviewer for promotions and tenure both nationally and globally, I continue to find the h-index to be a valuable adjunctive tool. As the authors point out, it is an objective metric that is quantitative and cumulative. It rewards durability and sustained productivity. However, they also criticize the h-index. It is true that highly cited papers do not carry more weight. My retort is that in addition to the h-index, one has to use other metrics such as the number of citations of the top 10 publications etc, because this also helps resolve the contribution to multiauthor papers to a major extent. In this competitive era of limited funding, team science is a solution and single-author papers are extremely rare in biomedical research. Self-citation is an issue and is more so with Google Scholar, which counts all citations including self-citations, as compared to Web of Science, which, to the best of my knowledge, excludes self-citation. This is exemplified by the inflated Google scholar h-index compared to Scopus (Mendeley) and Web of Science. In an interesting paper, Engqvist and Frommen2 state that tripling self-citations increase the h-index by only 1. There is clearly a difference in the h-index from natural sciences to biomedical sciences. It is interesting that a physicist proposed it as a metric of scientific achievement. The h-index is cumulative and cannot provide an indication of recent productivity. However, this is always available on PubMed and on the curriculum vitae of the candidate. Schreiber and Giustini1 chose to highlight the variance in h-index using Nobel laureates, although members of the National Academy of Sciences might be more relevant with a much larger sample size. In the American Journal of Clinical Pathology in 2010 the h-index was evaluated in the first sextile of medical colleges in the United States. Interestingly, in this report, chairs of pathology had the best mean score of 51.2 with a range of 28 to 83.3 This might be a prudent exercise for our fraternity to revisit in 2020 to see how present-day chairs fair. In conclusion, the h-index is one metric that is objective and judges the cumulative contribution of a scholar. It should be used in conjunction with the top publications of the author/candidate and other sources of information. In this regard, it is important to bear in mind that bias, academic envy, and other prejudices can be manifest in subjective references. The h-index has attracted a lot of attention since it was first proposed in 2005. Only a few papers that even mention the h-index have appeared in the pathology and laboratory medicine literature. Because it is likely to be used as a measuring stick for academic achievement, we thought that a review for this audience was timely. Our criticisms of the h-index have been presented by other authors as well.1,2 They are included to ensure a balanced perspective. Reducing one’s reputation to a number is overly simplistic—not every great scientist leaves a long trail of papers in his or her wake. For all of its elegance (so typical of the physicist’s mind), the h-index is only as good as the original data, which come from an individual’s publication record and the associated citations. A careful examination of authorship for each publication, assigning credit where it is due, may reveal more about a scientist’s lifetime contributions than the index itself. We thank Dr Jialal for noticing our paper and providing his comments.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,014 | 0,006 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,109 | 0,049 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».