Psycho-oncology best practice guidelines and a service perspective: conceptualising the fit and towards bridging the gap
Bibliographic record
Abstract
The purpose of this paper is two-fold. Firstly, it provides a summary of psycho-oncology best practice guidelines from the UK, US, Australia and Canada. The second aim of this paper is the examination of how a service in Ireland has progressed in adopting these best practice guidelines. A Working Model of Psychology in Action is presented to illustrate the interplay of components currently influencing psycho-oncology in context. The benchmarking process delineates efforts aimed at realising the best practice principles and is helpful in understanding the strengths and also the limitations of the service. The exploration of the challenges in applying non-national guidelines also highlights systemic dimensions such as contextual, organisational, professional and cultural dynamics. With a view to the future, gaps in the guidelines as well as current and potential aims in service development in psycho-oncology in the broader field are discussed.
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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 | 0.062 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".