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Record W2259488672 · doi:10.4103/2347-5625.160971

Perspectives from older adults receiving cancer treatment about the cancer-related information they receive

2015· article· en· W2259488672 on OpenAlexaff
Margaret I. Fitch, Alison McAndrew, Tamara Harth

Bibliographic record

VenueAsia-Pacific Journal of Oncology Nursing · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsCancerPaceMedicineConversationFamily medicineCancer treatmentHealth careHealth professionalsCancer survivorGerontologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Objective:Cancer patients have reported that information plays a significant role in their capacity to cope with cancer and manage the consequences of treatment. This study was undertaken to identify the importance older adults receiving cancer treatment assign to selected types of cancer-related information, their satisfaction with the cancer-related information they received, and the barriers to effective information provision for this age group.Methods:This study was conducted in two phases with separate samples. Six hundred and eighty-four older cancer patients receiving treatment completed a standardized survey and 39 completed a semi-structured interview to gather perspectives about cancer-related information. Data were analyzed for 65-79 years and 80+ year groups.Results:Information topics about their medical condition, treatment options, and side effects of treatment were rated as most important by the older cancer patients. Women assigned a higher importance ratings than men to information overall (t = 4.8, P < 0.01). Although participants were generally satisfied with the information, they received many described challenges they experienced in communicating with health care professionals because of the medical language and fast pace of speaking used by the professionals.Conclusions:The older cancer patients in this study endorsed the same topics of cancer-related information as most important as has been reported in studies for other age groups. However, this older group recommended that, during their interactions with older individuals, health care professionals use fewer medical words, speak at a slower pace, and provide written information in addition to the actual conversation. Cancer patients have reported that information plays a significant role in their capacity to cope with cancer and manage the consequences of treatment. This study was undertaken to identify the importance older adults receiving cancer treatment assign to selected types of cancer-related information, their satisfaction with the cancer-related information they received, and the barriers to effective information provision for this age group. This study was conducted in two phases with separate samples. Six hundred and eighty-four older cancer patients receiving treatment completed a standardized survey and 39 completed a semi-structured interview to gather perspectives about cancer-related information. Data were analyzed for 65-79 years and 80+ year groups. Information topics about their medical condition, treatment options, and side effects of treatment were rated as most important by the older cancer patients. Women assigned a higher importance ratings than men to information overall (t = 4.8, P < 0.01). Although participants were generally satisfied with the information, they received many described challenges they experienced in communicating with health care professionals because of the medical language and fast pace of speaking used by the professionals. The older cancer patients in this study endorsed the same topics of cancer-related information as most important as has been reported in studies for other age groups. However, this older group recommended that, during their interactions with older individuals, health care professionals use fewer medical words, speak at a slower pace, and provide written information in addition to the actual conversation.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.453
Teacher spread0.401 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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