Pharmacist's role in meeting the psychosocial needs of cancer patients using complementary therapy
Bibliographic record
Abstract
Complementary therapy is commonly used amongst cancer patients. The motivation for cancer patients to use complementary therapy is complex. Pharmacists at the British Columbia Cancer Agency are often called on to advise patients on the use of herbs and dietary supplements. However, they do not routinely address the psychosocial needs that motivate the patients to use these products. The most common factors involved are increased anxiety, need for information, maintenance of hope, a sense of control, negative experience with conventional medicine, and perceived holistic nature of complementary therapy. Pharmacists are in a position to identify and address some of the psychosocial issues, either directly or through referral to appropriate psychosocial counsellors. This includes screening for patients with significant anxiety, helping search for accurate information on conventional and complementary treatments, and maintaining a sense of hope and self control. The opportunity to provide basic psychosocial training to pharmacists should be explored, so that they may systematically assess and address the more common, simple psychosocial issues in cancer patients who seek to use complementary therapy. Given the propensity of distress in cancer patients, in general, this may provide potential benefits for patients seeking conventional and complementary therapies.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".