MétaCan
Menu
Back to cohort
Record W2008752804 · doi:10.1002/pon.836

Pharmacist's role in meeting the psychosocial needs of cancer patients using complementary therapy

2004· article· en· W2008752804 on OpenAlexaff
Mário L de Lemos

Bibliographic record

VenuePsycho-Oncology · 2004
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsPsychosocialReferralMedicineDistressAnxietyPharmacistAgency (philosophy)Cancer therapyFamily medicineCancerPsychotherapistPsychiatryNursingPsychologyClinical psychologyPharmacy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.452
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePsycho-OncologySame topicComplementary and Alternative Medicine StudiesFrench-language works237,207