Nigerian Medical Libraries Face Challenges With High Hopes for the Future
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.111 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".