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?Someone who cares:? A qualitative investigation of cancer patients' experiences of psychotherapy

2001· article· en· W1978820546 on OpenAlexaff
Terry MacCormack, Jo Simonian, Jacqueline Kyungah Lim, Louise Remond, Deonette Roets, Stewart M. Dunn, Phyllis Butow

Bibliographic record

VenuePsycho-Oncology · 2001
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Guelph
FundersNational Medical Research CouncilNational Health and Medical Research Council
KeywordsFeelingPsychotherapistPerspective (graphical)PsychologyQualitative researchCancerGrounded theoryCognitionMedicineClinical psychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Although psychotherapy for cancer patients is known to be effective, there is little in the research to indicate what elements of their therapy patients find most helpful. To explore this question, we interviewed cancer patients diagnosed with metastatic disease who had been offered two different forms of individual psychotherapy as part of a larger funded study. These interviews were then transcribed and analysed using grounded theory. Our aim was to explore patients' psychotherapy experience from their perspective and to determine what common elements in the two approaches they felt were of greatest benefit. Results indicated that patients offered cognitive behavioural therapy had similar experiences to those who received a type of relaxation therapy that included time for non-specific, patient-centred 'chat'. Central to participants' experiences was the opportunity both therapies gave them to enter a relationship in which they could safely share their thoughts and feelings with someone who seemed genuinely interested in understanding their cancer experience and 'truly cared'. These findings suggest that the unique perspectives of cancer patients can add considerably to our understanding of individual psychotherapy in cancer care settings and how this might be improved.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

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

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 head0.043
GPT teacher head0.420
Teacher spread0.377 · 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

Citations70
Published2001
Admission routes1
Has abstractyes

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