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Record W1575656347 · doi:10.2471/06.031567

La recherche bibliographique en médecine et santé publique: guide d’accès

2007· article· fr· W1575656347 on OpenAlexaboutno aff
Tomas Allen

Bibliographic record

VenueEurope PMC (PubMed Central) · 2007
Typearticle
Languagefr
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsTemptationMedical libraryWorld Wide WebLibrary scienceSet (abstract data type)Public healthMEDLINEComputer scienceInternet privacyMedicinePolitical sciencePsychology

Abstract

fetched live from OpenAlex

I recently read a novel set in an American college in which the author made numerous references to the main characters “googling” for information or having “googled” for all the research on their latest assignment. With the web so readily available, one might raise the question of why a book aimed at medical and public health researchers on recherche bibliographique (bibliographic searching) is needed, and one in French at that? Now that students and researchers have Google, or better yet the recently released beta version of Google Scholar, to find all the public health information on the web, why would anyone need something as old-fashioned as a book on bibliographic searching? Just enter a few keywords into any search engine and you have an unlimited amount of information at your fingertips (meme en francais). I would argue the need is probably greater now than ever before for a book on organized systematic searching. While huge depositories of information are readily available through a basic computer search, it is not always the best or most complete information. The temptation is to take what is available with just a few clicks on your mouse versus a visit to a library for a book or a journal article, or doing an in-depth and informed search. Researchers in the area of medicine and public health, however, need to be able to locate the most up-to-date, relevant and evidence-based information possible. To obtain this information requires a systematic methodology for attacking the huge amount of health literature and assuring that all the major and important sources are queried in order to obtain the best information Evelyn Mouillet has written a simple and well-laid-out guide for searching medical and health literature in a straightforward manner. The book begins with a discussion of how to search the basic sources. While basic searching need not be complex, it does require the kind of systematic approach which Mouillet presents in this book. She progresses through the major French-language health resources and then covers international sources (almost exclusively English). Important medical databases such as PubMed are covered with a clear explication of the differences between MEDLINE and PubMed. Also covered are bibliographic management software with emphasis on EndNote. The guide is made even more user-friendly by its inclusion of many illustrations and visual guides that mimic the computer screen during a search. Is there truly a need for guide such as this in French? Having covered the A to Z language spectrum, where I conducted library training sessions for an Albanian-speaking-only audience via a French interpreter, and having used Vietnamese versions of WHO publications during a library presentation, I welcome any documentation to assist in teaching researchers how to conduct in-depth searching. While the majority of the resources may be in English or use an English interface, it is beneficial for non-Anglophones to have access tools in their maternal language. Basic concepts of Boolean searching are better learnt in one’s most conversant language, and my personal observations over ten years of teaching reinforce this belief. A couple of changes would improve further editions. A stronger chapter devoted to defining the research question would make the book more useful to medical and public health researchers. As a librarian, my most important contacts with researchers are spent defining and probing their research questions, a step which involves breaking the question down into its various components. This vital step helps to determine which resources will be used and the breadth of resources to be searched. An overly broad question can lead to the frustrating situation of bringing up literally tens of thousands of hits. Refining the question allows for a more manageable number of articles, perhaps including the article that has already summarized and evaluated the best information from all those other articles. I also noticed a definite preference for resources located within France. This ignores the rich resources of Francophone research outside of France, including Belgium, Canada, Switzerland and Francophone Africa, all of which offer relevant, high-quality research. Africa Index Medicus (AIM), for example, is a bibliographic database that indexes many Francophone African journals and reports, often with links to the articles’ full text. Sole reliance on the French databases, even with supplementation from PubMed and Embase, almost ensures that researchers will miss relevant research, because research from developing countries may not be indexed by the major databases or there can be significant delays from when the research is published until it appears in the major databases. Evelyne Mouillets’s book is, however, one of the better books to guide readers in searching for medical and health information that I have read. Hopefully the publisher will come out with a translation, as not only is it an excellent guide to searching, but it would also be useful to have an English version to encourage researchers to explore valuable French-language resources. ■

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.060
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0160.015
Science and technology studies0.0030.006
Scholarly communication0.0160.017
Open science0.0030.006
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0490.089

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.

Opus teacher head0.255
GPT teacher head0.485
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

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Citations1
Published2007
Admission routes1
Has abstractyes

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