Non-medical cancer support for Qatari women
Bibliographic record
Abstract
Patients' perceptions of the importance of psychosocial support is becoming increasingly important for improving the quality of patient-centred care, but information about the attitudes of female cancer patients in Arab and Muslim populations is scarce. So Razzan Alagraa of Sheffield University and her colleagues investigated the extent to which female cancer patients in Qatar view psychosocial care as important, publishing their findings in the Qatar Medical Journal . They administered Arabic and English-language questionnaires to a total 133 women, all of whom had been diagnosed with cancer at the National Center for Cancer Care and Research in Qatar. The questionnaire was designed to obtain socioeconomic information such as age, nationality, religion, level of education, and marital status. It also included questions that surveyed the patients' views about four types of psychosocial support: physician-referred support, family support, support groups, and religious or spiritual support, and required them to rate the importance of each. Of the 133 women surveyed, only four declined to take part. Roughly half of the respondents spoke Arabic, and the other half spoke English. The majority of them (~60%) self-identified as Muslim, with the most common diagnosis being breast cancer. Approximately two thirds of the respondents indicated that they would like to have some of the support services mentioned, compared to one third who answered 'no'. One fifth stated that they would like physician-referred support, and another fifth said they would like religious support. Only 13% indicated that they would like to receive all four types of support, while just over one quarter did not indicate a preference. Qatar does not have a cancer registry, so the study provides valuable demographic information that could be useful for future studies of cancer in Qatar and the wider Middle East region. “The most important question is how to translate our findings into services that will actually be used by the patient population,” says Alagraa. “Certain areas of support may be rated high in terms of importance, but the services may not be used by patients for various reasons, [such as] poor design and implementation of programmes or lack of patient input, so we would like to conduct one-on-one interviews with patients to get a sense of what specific aspects patients would like see in a support service.”Other InformationPublished in: QScience.com Highlights, Published by Nature Research for Hamad bin Khalifa University Press (HBKU Press) License: http://creativecommons.org/licenses/by/4.0
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".