Qualitative assessment of information and decision support needs for managing menopausal symptoms after breast cancer
Bibliographic record
Abstract
PURPOSE: For breast cancer (BrCa) survivors, premature menopause can result from conventional cancer treatment. Due to limited treatment options, survivors often turn to complementary therapies (CTs), but struggle to make informed decisions. In this study, we identified BrCa survivors' CT and general information and decision-making needs related to menopausal symptoms. METHODS: The needs assessment was informed by interpretive descriptive methodology. Focus groups with survivors (n = 22) and interviews with conventional (n = 12) and CT (n = 5) healthcare professionals (HCPs) were conducted at two Canadian urban cancer centers. Thematic, inductive analysis was conducted on the data. RESULTS: Menopausal symptoms have significant negative impact on BrCa survivors. Close to 70 % of the sample were currently using CTs, including mind-body therapies (45.5 %), natural health products (NHPs) and dietary therapies (31.8 %), and lifestyle interventions (36.4 %). However, BrCa survivors reported inadequate access to information on the safety and efficacy of CT options. Survivors also struggled in their efforts to discuss CT with HCPs, who had limited time and information to support women in their CT decisions. Concise and credible information about CTs was required by BrCa survivors to support them in making informed and safe decisions about using CTs for menopausal symptom management. CONCLUSIONS: High quality research is needed on the efficacy and safety of CTs in managing menopausal symptoms following BrCa treatment. Decision support strategies, such as patient decision aids (DAs), may help synthesize and translate evidence on CTs and promote shared decision-making between BrCa survivors and HCPs about the role of CTs in coping with menopause following cancer treatment.
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 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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".