Roles and competencies of the clinical psychologist in adult diabetes care—A consensus report
Bibliographic record
Abstract
AIMS: Psychological care is recognised as an integral part of quality diabetes care. We set out to describe the roles and competencies of the clinical psychologist as a member of the multidisciplinary adult diabetes care team, focused on secondary care. METHODS: The authors are clinically experienced psychologists involved in adult diabetes care, from Australia, Europe and North America, and active members of the international psychosocial aspects of diabetes study group. Consensus was reached as a group on the roles and competencies of the clinical psychologist working in adult diabetes secondary care, building both on expert opinion and a selective review and discussion of the literature on psychological care in diabetes, clinical guidelines and competency frameworks. RESULTS: The clinical psychologist fulfils multiple roles: (1) as a clinician (psychological assessment and therapy), (2) as advisor to the healthcare team (training, consulting), (3) as a communicator and promotor of person-centred care initiatives and (4) as a researcher. Four competencies that are key to successfully fulfilling the above-mentioned roles in a diabetes setting are as follows: (a) specialised knowledge, (b) teamwork and advice, (c) assessment, (d) psychotherapy (referred to as STAP framework). CONCLUSIONS: The roles and competencies of clinical psychologists working in diabetes extend beyond the requirements of most university and post-graduate curricula. There is a need for a comprehensive, accredited specialist post-graduate training for clinical psychologists working in diabetes care, building on the proposed STAP framework. This calls for a collaborative effort involving diabetes organisations, clinical psychology societies and diabetes psychology interest groups.
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 | 0.076 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| 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".