The health coaching competency gap – Analysis of pharmacist competency frameworks from Australia, Canada, New Zealand the United Kingdom and the United States of America
Bibliographic record
Abstract
The traditional competency frameworks for coaches, the International Coaching Federation (ICF) and the European Mentoring and Coaching Council (EMCC) disregard the differences in expertise required among the diverse professions that may provide coaching. A recent systematic review has identified competencies specific to health professionals who health coach. There are increasing workload pressures in primary care; pharmacists can potentially shift to the greater provision of health promotion services, such as health coaching. The provision of such services needs to be underpinned by competency frameworks, which support the role of pharmacists as health coaches. This analysis identifies the competency gaps for pharmacists if they are to take on the role of health coaching. The enabling competencies of health coaches were compared to the competency frameworks of pharmacists from Australia (AUS), Canada (CAN), New Zealand (NZ), the United Kingdom (UK) and the United States of America (USA). Correlations between the international pharmacist competency frameworks and the competencies enabling health coaching showed that entry to practice pharmacists from AUS, CAN and NZ all require training enabling the health coaching competency 'demonstrates confidence', whereas competency frameworks for pharmacists from both the UK and the USA included all competencies required to health coach. Although pharmacists from the countries examined had most of the competencies required to health coach, gaps within the international pharmacist competency frameworks were apparent, university curricula addressing these gaps would equip entry to practice pharmacists with the knowledge and understanding to confidently provide emerging professional pharmacy services such as health coaching.
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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".