‘INSPIRED’ COPD Outreach Program<sup>TM</sup>: Doing the Right Things Right
Bibliographic record
Abstract
The well-documented gaps between needed and provided care for patients and families living with chronic obstructive pulmonary disease (COPD) mandate changes to clinical practice. The multifaceted evidence-based INSPIRED COPD Outreach ProgramTM was first implemented in Halifax, Nova Scotia, Canada in 2010 (INSPIRED = Implementing a Novel and Supportive Program of Individualized care for patients and families living with REspiratory Disease) and undergoes ongoing evaluation. By enhancing patient confidence to manage their illness more effectively in their homes and communities, there has been a sustained and substantial reduction in facility-based care in comparison with patient care experience pre-INSPIRED. Sustaining and spreading a program recently designated a leading practice by Accreditation Canada, and especially modifying the program as new evidence emerges, requires integrating and modeling at the ‘bedside’ both evidence-based medicine (‘doing the right things’) and quality improvement (‘doing them right’). In Canada, where COPD care gaps are common, a new pan-Canadian INSPIRED-based quality improvement program is supporting multidisciplinary healthcare teams to bridge the chasm between evidence and practice by working together to ‘do the right things right’ in COPD care.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".