Bibliographic record
Abstract
Many libraries have adopted Twitter to connect with their clients, but the library literature has only begun to explore how health libraries use Twitter in practice. When presented with new responsibility for tweeting on behalf of her library, the author was faced with the question “what do other health libraries tweet about?”. This paper presents a content analysis of a sample of tweets from ten health and medical libraries in Canada, the United States, and the United Kingdom. Five hundred twenty-four tweets were collected over 4 one-week periods in 2014 and analyzed using a grounded theory approach to identify themes and categories. The health libraries included in this study appear to use Twitter primarily as a current awareness tool, focusing on topics external to the library and its broader organization and including little original content. This differs from previous studies which have found that libraries tend to use Twitter primarily for library promotion. While this snapshot of Twitter activity helps shed light on how health libraries use Twitter, further research is needed to understand the underlying factors that shape libraries’ Twitter use. Beaucoup de bibliothèques ont choisi d’utiliser Twitter pour communiquer avec leurs clients, mais la littérature a commencé à peine à explorer comment des bibliothèques de la santé utilisent Twitter dans la pratique. Lorsqu’on lui a présenté la nouvelle responsabilité de s’occuper du compte Twitter pour la bibliothèque, l’auteure s’est demandé « qu'est-ce que d’autres bibliothèques de la santé disent sur Twitter ? ». Cet article présente une analyse du contenu d’un échantillon de Tweets de dix bibliothèques médicales au Canada, aux États-Unis et au Royaume-Uni. 524 Tweets ont été recueillis au cours de quatre périodes d’une semaine en 2014 et ont été analysés selon une théorie ancrée afin d’identifier des thèmes et des catégories. Les bibliothèques de la santé incluses dans l’étude paraissent utiliser Twitter principalement comme outil de sensibilisation, se concentrant sur des sujets en dehors de la bibliothèque et l’organisation en général, et comprenant peu de contenu original. Cela se différencie d’autres études qui ont trouvé que les bibliothèques sont enclines à utiliser Twitter principalement pour la promotion de la bibliothèque. Bien que cet aperçu d’activité sur Twitter aide à éclairer la façon dont des bibliothèques l’utilisent, une recherche plus approfondie est nécessaire afin de comprendre les facteurs sous-jacents qui touchent l’usage de Twitter par des bibliothèques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".