Notice bibliographique
Résumé
In their paper, “Measuring Information Technology Investment among Canadian Academic Health Sciences Centres,” one gets the impression that Pederson and Leonard felt a need to share their frustrations. Not only was their task difficult (i.e., trying to get a meaningful grip on how much is being spent on information technology), but the lack of co-operation they encountered must indeed have been exasperating. Having also experienced the “reluctance-to-reveal” phenomenon, this commentator can sympathize with their trials and tribulations. Determining the true cost of information technology (IT) across organizations is indeed a difficult task for a number of reasons. First, as Pederson and Leonard point out, there is little consistency across Canadian healthcare organizations as to what is to be included in the IT domain, let alone the information management (IM) domain. As part of a fourth-year course taught at the University of Victoria in 2004, 28 Chief Information Officers (CIOs) were interviewed by students and asked to describe the departments for which they were responsible. The survey found that the CIOs were heading divisions that had 17 different names, with information management leading the way – used in four sites. To say that our Canadian healthcare CIOs are responsible for a diverse set of departments is an understatement. Areas of responsibility range from the usual information management and technology (IM&T) to others such as networks, health records, decision support, telecommunications, biomedical engineering services, switchboard and information desk, library services and privacy. The areas for which the CIOs were responsible generated a list that was two pages long! Little wonder it is difficult to find a common set of measurements as to what the IT investment really is. A second reason that it is challenging to measure the value of IT investments is because, as Pederson and Leonard point out, “Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) find themselves under increasing pressure to defend the value proposition of IT.” A recent paper by Bend from the Institute of Public Policy Research in England, entitled, “Public Value and eHealth,” puts it even more bluntly: “Despite the clear potential, really solid evidence of a positive impact of IT in practice is still quite scarce.” This is not a conducive climate for measuring and revealing one’s true costs.
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 | 0,080 | 0,256 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,010 | 0,018 |
| Études des sciences et des technologies | 0,007 | 0,021 |
| Communication savante | 0,016 | 0,028 |
| Science ouverte | 0,006 | 0,011 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».