“Swamped with information”: a qualitative study of family physicians' experiences of managing and applying pandemic-related information
Bibliographic record
Abstract
Introduction Providing family physicians (FPs) with the information they need is crucial for their participation in a coordinated pandemic or health emergency response, and to allow them to effectively run their practices. Most pandemic planning documents do not address communication plans specific to FPs. This study describes FPs' experiences and challenges with information management during the COVID-19 pandemic in Canada. Methods We conducted semi-structured qualitative interviews with FPs across four Canadian regions and asked about their roles during different pandemic stages, as well as facilitators and barriers they experienced in performing these roles. We transcribed the interviews, used a thematic analysis approach to develop a unified coding template across the four regions, and identified recurring themes. Results We interviewed 68 FPs and identified two key themes specifically related to communication. The first is FPs' experiences obtaining and managing information during the COVID-19 pandemic. FPs were overwhelmed by the volume of information and had difficulty applying the information to their practices. The second is the specific attributes FPs need from the information sent to them. Participants wanted summarized and consistent information from credible sources that are relevant to primary care. Discussion Providing clear, collated, and relevant information to FPs is essential during pandemics and other health emergencies. Future pandemic plans should integrate strategies to deliver information to FPs that is tailored to primary care. Findings highlight the need for a coordinated communication strategy to effectively inform FPs in health emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".