Employers’ Perspectives on Future Roles and Skills Requirements for Australian Health Librarians
Bibliographic record
Abstract
Objective – This study, which comprises one stage of a larger project (ALIA/HLA Workforce and Education Research Project), aimed to discover employers’ views on how (or whether) health librarians assist in achieving the mission-critical goals of their organizations; how health librarians contribute to the organization now and into the future; and what are the current and future skills requirements of health librarians. Methods – Each member of the project group approached between one and five individuals known to them to generate a convenience sample of 22 employers of health librarians. There were 15 semi-structured interviews conducted between October and November 2010 with employers in the hospital, academic, government, private, consumer health and not-for-profit sectors. The interview schedule was sent to each interviewee prior to the interview so that they had time to consider their responses. The researchers wrote up the interview notes using the interview schedule and submitted them to the principal researcher, who combined the data into one document. Content analysis of the data was used to identify major themes. Results – Employers expressed a clear sense of respect for the roles and responsibilities of library staff in their organizations. Areas of practice such as education and training, scientific research and clinical support were highlighted as critical for the future. Current areas of practice such as using technology and systems to manage information, providing information services to meet user needs and management of health information resources in a range of formats were identified as remaining highly relevant for the future. There was potential for health librarians to play a more active and strategic role in their organizations, and to repackage their traditional skill sets for anticipated future roles. Interpersonal skills and the role of health librarians as the interface between clinicians and information technology were also identified as critical for the future. Conclusions – Interviews with employers provided valuable insights into the current and future roles and skills requirements of health librarians in Australia, enriching the findings of the earlier stages of the research project. The next step is to work with the stakeholder groups in this project and use the research project’s findings as the evidence base on which to develop a structured, modular education framework comprising a postgraduate qualification in health librarianship and a continuing professional development structure supporting a three-year cycle of certification and revalidation.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".