Using the theory of planned behavior to understand health professionals' attitudes and intentions to refer cancer patients for psychosocial support
Bibliographic record
Abstract
OBJECTIVE: To describe oncology professionals' patterns of referral to existing community and psychosocial support services, including complementary therapies utilizing the theory of planned behavior (TPB). METHODS: An exploratory cross-sectional survey of 72 oncology professionals including nurses (73.6%), medical practitioners (19.4%) and allied health professionals (6.9%) from health institutions in South Australia assessed past referral patterns, perceived attitudes of peers, control over and attitudes toward, referral, past referral practices and how these impact on intention to refer. RESULTS: Referral to support services such as a cancer helpline, allied health or complementary services was infrequent. A hierarchical regression entering awareness, past referral and the TPB variables (attitude, subjective norm and perceived control) explained 51% of the variance on the outcome 'intention to refer'. Barriers to referral for support included lack of local services for remote patients, and financial considerations. CONCLUSION: Interventions with health professionals should focus on the development of a culture, which recognizes the importance of addressing a breadth of patient needs across the cancer trajectory. Education and support for health professionals is required to ensure that they feel comfortable discussing support needs and referring to appropriate support services.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".