Nigerian Medical Libraries Face Challenges With High Hopes for the Future
Notice bibliographique
Résumé
A Review of: Popoola, B., Uzoagba, N., & Rabiu, N. (2020). “What’s happening over there?”: A study of the current state of services, challenges, and prospects in Nigerian medical libraries. Journal of the Medical Library Association, 108(3), 398–407. https://doi.org/10.5195/jmla.2020.607 Abstract Objective – This study examined the field of medical librarianship as it is currently practiced in Nigeria. Design – Mixed methods: electronic survey and in-person interview. Setting – The survey was advertised via an email list and a WhatsApp discussion group, both based in Nigeria. The interviews were requested directly by the authors. Subjects – Librarians working in medical libraries in Nigeria for the survey; library heads for the interviews. Methods – The survey was created in Google Forms and shared via the Nigerian Library Association’s email discussion list and the WhatsApp Group for the Medial Library Association of Nigeria. Question categories included personal and library demographics, library patronage/social media use, library services for users, and librarians’ training and challenges. Most questions were closed-ended. Survey data was analyzed in SPSS for response frequencies and percentages. The interviews were conducted in person. Questions covered topics such as demographics, challenges, and prospects (for medical librarianship in Nigeria). Interview transcriptions underwent thematic content analysis. Main Results – The majority of the 58 survey respondents (73%) reported seven or more years of medical library experience. There was no consensus on classifications schemes used throughout medical libraries in Nigeria, with 43% using the US National Library of Medicine classification and 32% using the Library of Congress. Social media use also varied, but the majority (approximately 45%) reported using social media less than monthly to promote their libraries or programming. Monographs were the main collection material reported by roughly 35% of respondents. Journals followed at approximately 24% while only 10% reported electronic resources as the main collection material. The majority of respondents (53%) noted that their library did not offer specialized services. Others (31%) reported “selective dissemination of information, current awareness services, or reference services” (p. 402) as specialized services; 7% reported literature searching. The majority of respondents (70-75%) rated their skill levels in evidence based medicine and systematic reviews as beginner/intermediate. Half of respondents reported that their libraries had not held any training programs or seminars for library users in the six months prior. Interviews with library heads revealed that they all had high hopes for the future of medical libraries in Nigeria but also noted many challenges. These included a lack of cooperation between libraries, a lack of interlibrary loan services, budget deficiencies, and insufficient access to the internet. This mirrored survey responses, 50% of which noted access to electronic information was a “significant barrier to improved services” (p. 402) along with a lack of training (53%) and low library usage (57%). Conclusion – Medical libraries in Nigeria face multiple challenges. Budgetary constraints, a lack of library cooperation, and internet accessibility limit the availability of electronic collections. The authors suggest that library associations in Nigeria focus on education and training opportunities for current and future medical librarians.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,111 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».