Towards optimal management of health information users' feedback: the case of the canadian pharmacists association
Bibliographic record
Abstract
There is increasing attention to information users' feedback comments as they can be used to improve information resources. In contexts where information resources are rich in knowledge, optimal user feedback management is crucial for the information provider to make sure that users' information needs are met.In this dissertation, I worked with the Canadian Pharmacists Association (CPhA), which regularly uses health professionals' feedback to improve its publications. The CPhA wants an appropriate process to enable user feedback management in an effective and efficient manner. Thus, the present research addresses the overarching question "How can user feedback management be optimized for the CPhA?" The problem of how to optimize the management of user feedback was conceptualized in three parts: (1) the feedback comments, (2) the feedback management process, and (3) the factors affecting the development and implementation of optimal user feedback management in the organizational setting. The conceptual framework is derived from information studies, management science and organizational studies. A participatory action research approach was taken to conduct an organizational case study, using qualitative methods such as interview, observation, and document analysis.Research findings provide empirical evidence revealing four types of value of pharmacists' feedback comments to the CPhA, nine key issues in its user feedback management process, and twenty six factors affecting the innovation of user feedback management. Main contributions of this dissertation are as follows: this study empirically examined the usefulness of user feedback comments based on a value perspective in philosophy; two conceptual frameworks were proposed and demonstrated as relevant to studying information use and the related innovation in an organizational setting; and lessons have been learned from a comprehensive examination of the factors that affect innovation processes related to organizational information use.
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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 | 0.062 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.041 | 0.023 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".