Women living with ovarian cancer described changes in day to day living, major challenges, and sources of support
Bibliographic record
Abstract
Howell D, Fitch MI, Deane KA. Impact of ovarian cancer perceived by women. Cancer Nurs2003 ; 26 : 1 –9 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: What are the perceptions of women living with ovarian cancer? Qualitative study. Toronto, Ontario, Canada. 18 women (age range 35–73 y) with ovarian cancer were identified through 2 major cancer centres and a local ovarian cancer support group. Data were collected through 1 hour semistructured telephone interviews. Questions related to the effects of ovarian cancer on day to day living, major challenges, and sources of support. Interview data were transcribed verbatim and reviewed by the 3 authors, who identified themes arising from an inductively derived coding scheme. (1) Effect of cancer on day to day living. Cancer profoundly altered women’s daily lives, resulting in the loss of usual activities, inability to work, and financial concerns arising because of uncertainty about future health needs and employment. Women struggled to integrate changes and experienced emotional distress as they realised that life was changed forever. The diagnosis of ovarian cancer had an impact on the family . Women described changing roles and fears for husbands . The marital relationship became strained as husbands adopted new roles and responsibilities to support their wives and maintain the household. Unspoken fears affected communication, and women worried about the effects of added stress on their husbands’ health. Altered sexuality occurred because of treatment related … [1]: {openurl}?query=rft.jtitle%253DCancer%2Bnursing%26rft.stitle%253DCancer%2BNurs%26rft.aulast%253DHowell%26rft.auinit1%253DD.%26rft.volume%253D26%26rft.issue%253D1%26rft.spage%253D1%26rft.epage%253D9%26rft.atitle%253DImpact%2Bof%2Bovarian%2Bcancer%2Bperceived%2Bby%2Bwomen.%26rft_id%253Dinfo%253Adoi%252F10.1097%252F00002820-200302000-00001%26rft_id%253Dinfo%253Apmid%252F12556707%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1097/00002820-200302000-00001&link_type=DOI [3]: /lookup/external-ref?access_num=12556707&link_type=MED&atom=%2Febnurs%2F6%2F4%2F126.atom [4]: /lookup/external-ref?access_num=000180834000001&link_type=ISI
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".