A Breast Reconstruction Needs Assessment: How does Self-Efficacy Affect Information Access and Preferences?
Bibliographic record
Abstract
BACKGROUND: Breast cancer patients requiring mastectomy do not consistently receive information about post-mastectomy breast reconstruction (PMBR) surgery from the treatment team. Patients have varying levels of self-efficacy, defined as one's confidence in their ability to gather information and make health-related decisions. The present preliminary study was designed to evaluate the relationship between self-efficacy and access to PMBR information. METHODS: A qualitative interview study was conducted on a convenience sample of 10 breast cancer patients considering or having already undergone PMBR and six key health care provider informants. The modified six-item Stanford Self-Efficacy Scale for managing chronic disease was administered. RESULTS: Patient self-efficacy scores ranged from 5 to 9.3 (out of 10). Two main access to information themes were identified from the patient qualitative data: theme A - difficulty initiating the PMBR discussion; and theme B - perceived lack of access to PMBR information with the sub-themes of timing, modality, quantity and content of resources. All respondents expressed their concern over the absence of a standardized process for initiating the dialogue of PMBR. Patients also reported that credible and easily accessible information was not routinely available and expressed a desire to hear about their PMBR options early in the decision-making process. CONCLUSIONS: Health care providers may need to assume more responsibility in standardizing information dissemination on PMBR. This information should be distributed early in the consultation process, the content should be complete, and there may be a role for individualizing the delivery of information based on a patient's level of self-efficacy.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".