Making decisions in advanced cancer : the lived experience of women and their relevant others
Bibliographic record
Abstract
This descriptive phenomenology had two purposes: first, to explore the experience of making decisions for women with advanced cancer; and second, to explore the experience for significant others and health care team members as women made their decisions.A plethora of research exists on making decisions during the cancer experience, including research regarding: 1) decision-making styles; 2) factors or determinants which play a role in decision making; 3) information: needs, seeking behaviours, and utilization; and 4) decision support technologies.However, a gap exists in the literature regarding the experience of making decisions.Conversational interviews were conducted with five women and three relevant others for each woman: her primary nurse, her oncologist, and one significant other.Women were also provided with the opportunity to journal in a diary or email their memories of decisions and the surrounding experience.Van Manen's (1990) phenomenology guided the analysis of data.For the women, analysis centered on the four existentials of lived time, lived other, lived space, and lived body, revealing four themes of the lived experience of making decisions: 1) control, 2) influence, 3) normalcy, and 4) vulnerability.Phenomenological analysis on data from the significant others revealed three themes: 1) what used to be, 2) power shift, and 3) 'life on hold.' Themes for the health care team's experience as women made decisions were: 1) emotional detachment, 2) discomfort, and 3) acquiescing.Understanding the perspectives from these lived experiences will assist the health care team to support women, and their significant others, through the experience of making decisions.iii ACKNOWLEDGMENTS First, I must thank the women, men, and my team members from the Saskatoon Cancer Clinic.Thank you for the gift of your stories.Thank you to Dr. Muriel Montbriand for delivering an interesting qualitative research course.Without those first moments of creative discussion, and your appreciation for van Manen's and van den Berg's phenomenologies, the proposal for this research could have never moved forward.Thank you also for choosing to be my supervisor, providing guidance and sharing your expertise.Thank you to Dr. Wendy Duggleby for being my supervisor in the end.I appreciate your drive and energy, your directness, and your constant communication.Just as when you were my undergrad preceptor, you continue to exemplify the role of
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".