Finding a needle in a haystack: The identification of clinical practice guidelines for psychosocial oncology through an environmental scan of the academic and gray literature
Bibliographic record
Abstract
OBJECTIVE: Clinical practice guidelines (CPGs) are evidence-based tools well-suited to translate the latest research evidence into recommendations for routine clinical care. Given the rapid expansion of psychosocial oncology research, they represent a key opportunity for informing the treatment decisions of overburdened clinicians, standardizing service delivery, and improving patient-reported outcomes. Yet, there is little consensus on how clinicians can most effectively access these tools and little to no information on the current availability and scope of CPGs for the range of psychosocial symptoms and concerns experienced by patients with cancer. METHOD: Our environmental scan consisted of an academic and gray literature designed to identify currently available CPGs addressing a range of cancer-related psychosocial symptoms. RESULTS: Findings revealed a total of 23 existing psychosocial oncology CPGs that met full eligibility criteria. The gray literature search was found to be more effective at identifying CPGs (n = 22) compared to the academic search (n = 9). CONCLUSION: Several concerns arose from the systematic search. The limited publication of CPGs in peer-reviewed journals may make clinicians and stakeholders more hesitant to implement CPGs due to uncertainties about the methodological rigor of the development process. Further, many existing CPGs are outdated or failed to be updated according to guideline recommendations, meaning that the recommendations may fall short of their purpose to translate up-to-date research findings. FUTURE DIRECTIONS: Future research should seek to systematically assess the quality of existing psychosocial oncology CPGs and shed light on the current state of implementation and adherence in clinical practice in order to better inform guideline developers on the current needs of the psychosocial oncology community.
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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 | 0.222 | 0.611 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".