PSYCHOLOGY EDUCATION WORKSHOPS ARE AN INNOVATIVE METHOD OF OFFERING PSYCHOLOGICAL INPUT TO A WIDER POPULATION
Bibliographic record
Abstract
Abstract AIMS People diagnosed with brain tumours are well recognised as needing psychological support. Service provision is limited and can be diffcult to gain engagement. Providing psychology education workshops aims to be inclusive, bring patients and loved ones together and offer an opportunity for peer support. METHOD Patients who had finished or were coming to the end of their current treatment were invited to attend a psychology education workshop. Their loved ones were also invited to attend via the patient’s invitation letter. A clinic code was set up to enable us to monitor who was invited, who attended and importantly who did not. This also aimed to integrate the workshop into standard of care on completion of treatment. Feedback was obtained from attendees at the end of the workshop verbally as well as offering the opportunity to give anonymous written feedback. RESULTS On average a third of people invited attended each group. Half of these attended with one or more loved ones. 100% of attendees found the workshop helpful. Feedback included “Useful to realise that my emotions are similar to his” – Carer and “Having the shared experience of being here with (loved one) has started to open things up for us as a family” – Family member. CONCLUSIONS Patients and carers valued the workshop. This format offered a more accessible introduction to psychology. The workshop was enough to provide some patients and carers with strategies to look after their psychological well-being and served as a method of identifying those with greater need.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".