Correction: Exploring health professionals' views on the depiction of conversational agents as health professionals: a qualitative descriptive study
Bibliographic record
Abstract
Correction on: MacNeill AL, MacNeill L, Luke A and Doucet S (2025) Exploring health professionals' views on the depiction of conversational agents as health professionals: a qualitative descriptive study. Front. Digit. Health 7:1590514. doi: 10.3389/fdgth.2025.1590514 In the published article, some research findings were incorrectly identified as anecdotal evidence. In the same sentence, the discussed research findings aligned with one citation but not the other.A correction has been made to Discussion, Comparison with Prior Work, paragraph 1, to address both points. The sentence in question previously stated:"Moreover, there is some anecdotal evidence that people interacting with HCCAs can mistake them for real health professionals, even after they are explicitly told that these programs are not actual providers (21,37)."The corrected sentence appears below: "Moreover, there is evidence that people can experience confusion or misunderstanding over whether HCCAs are real care providers, even after they are explicitly told that these programs are not actual providers (21, 37)."The change does not affect the scientific conclusions of the article. The original article has been updated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".