Community partners identified implementation considerations prior to a randomized clinical trial for uncontrolled asthma in Federally Qualified Health Centers
Bibliographic record
Abstract
Background and Purpose: Federally Qualified Health Centers (FQHC) are critically important in addressing the unmet healthcare needs of individuals impacted by poverty. We used implementation science frameworks to advance understanding of perceived and actual facilitators and barriers to a novel asthma intervention before initiating a FQHC practice-based clinical trial. Methods: Interviews with clinicians and administrators explored pre-implementation trial considerations. Transcripts were inductively coded using conventional content analysis. Results: Sixteen administrators and/or clinicians (88% female; mean age 49 ± 12.21; 44% Black race; 25% Hispanic ethnicity) from four FQHCs participated. Themes included (1) multi-level factors making successful implementation more or less likely, (2) pandemic-specific concerns with implications for current healthcare delivery challenges, and (3) unintended implementation consequences. Conclusions: Participants were optimistic about the likelihood of successful intervention implementation if challenges were recognized and managed. Combined with other planned assessments, this data may provide a more comprehensive evaluation of clinical trial implementation in FQHCs.
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 | 0.305 | 0.372 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".