MétaCan
Menu
Back to cohort
Record W6989346652

Assessing the needs of people living with cancer on Prince Edward Island: A psychosocial approach

2007· article· en· W6989346652 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialInformation needsCancerNeeds assessmentSocial supportService (business)Social needsHealth careHealth professionals
DOInot available

Abstract

fetched live from OpenAlex

The needs of 139 people living with cancer in Prince Edward Island were examined in this cross-sectional descriptive quantitative study. Primarily close-ended telephone interviews were used to identify the informational, practical and financial, as well as emotional, spiritual, and social needs of cancer patients. Level of health care service accessibility, the degree and desired mechanism in which needs are being, and can be, met were also explored. Overall, information needs ranked high among cancer patients in Prince Edward Island. Over half of patients expressed a need for several types of information. The two most common places patients look for information are health care professionals (66%) and the Internet (50%), and patients prefer to receive information via pamphlets and written materials (86%). Managing side effects such as pain, nausea, and fatigue (30%) was the most often reported practical need, as well as the most often reported unmet practical need (40%). Between 25% and 30% of patients reported several needs related to emotional, spiritual and social concerns, with roughly half of patients stating that these needs had not been met. Over half of participants (57%) indicated that they would like to receive support through individual visits with a cancer survivor, or through access to a central staff person that could guide cancer patients through their cancer experience (53%). Many patients stated that their health care services needs were accessible all of the time.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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 designObservational
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueIslandScholar (University of Prince Edward Island)Same topicCancer survivorship and careFrench-language works237,207