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.
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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 | 0.017 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.058 | 0.032 |
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".