Developing a Library Association Membership Survey: Challenges and Promising Themes
Bibliographic record
Abstract
Objective – Many of us involved in the library and information sector are members of associations that represent the interests of our profession. These associations are often key to enabling us to provide evidence based practice by offering opportunities such as professional development. We invest resources in membership so we must be able to inform those in charge about our needs, expectations, and level of satisfaction. Governing bodies and committees, therefore, need a method to capture these views and plan strategy accordingly. The committee of the Health Sciences Libraries Group (HSLG) of the Library Association of Ireland wanted to enable members to give their views on the group, to understand what aspects of a library association are important to librarians in Ireland, and to learn about the reasons for and against membership. Methods – Surveys are a useful way of obtaining evidence to inform policy and practice. Although relatively quick to produce, their design and dissemination can pose challenges. The HSLG committee developed an online survey questionnaire for members and non-members (anyone eligible to join our library association). We primarily used multiple choice, matrix, and contextual/demographic questions, with skip logic enabling choices of relevance to respondents. Our literature review provided guidance in questionnaire design and suggested four themes that we used to develop options and to analyse results. Results – The survey was made available for two weeks and we received 49 eligible responses. Analysis of results and reflection on the process suggested aspects that we would change in terms of the language used in our questionnaire and dissemination methods. There were also aspects that show good potential, including the four themes that were used to understand what matters to members: expertise (professional development), community (connecting and engaging), profession (sustaining and strengthening), and support (financial and organizational supports). Overall, our survey provided rich data that met our objectives. Conclusion – It is essential that those who are governing any group make evidence based decisions, and a well-planned survey can support this. Our article outlines the elements of our questionnaire and process that didn’t work, and those that show promise. We hope that lessons learned will help anyone planning a survey, particularly associations who wish to ascertain the views of their members and others who are eligible to join. With some proposed modifications, our questionnaire could provide a template for future study in this area.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.245 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".