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Record W2898890760 · doi:10.46743/2160-3715/2018.3497

A Critical Analysis of the Delivery of a Psychosocial Workshop for Cancer Survivors with Lymphedema

2018· article· en· W2898890760 on OpenAlexafffund
Ryan Hamilton, Roanne Thomas, Yvonne Anisimowicz, Marquelle Piers, Renee Matte

Bibliographic record

VenueThe Qualitative Report · 2018
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsUniversity of OttawaUniversity of New Brunswick
FundersCanada Research ChairsFondation de la recherche en santé du Nouveau-Brunswick
KeywordsPsychosocialLymphedemaCoping (psychology)PsychologyFacilitationPsychotherapistBiopsychosocial modelMedicineBreast cancerCancer

Abstract

fetched live from OpenAlex

Secondary lymphedema is a chronic condition that can develop after the treatment of cancer and can often lead to negative psychological and social impairments. When dealing with chronic illness, hoping and coping are interdependent. Previous research has assessed the outcomes of workshops designed to enhance hope but has not examined the workshop itself to determine how those outcomes were achieved. This study deconstructs the Living Hopefully with Lymphedema workshop to identify (1) what aspects of the workshop facilitated or interfered with therapeutic progress, (2) key aspects of facilitation that contributed to the functioning of the workshop, and (3) how participants responded to the workshop. Two three-day workshops were attended by a total of 19 participants. All sessions were audio taped and the recordings analyzed. Theoretical coding revealed a central theme focused on the importance of a safe environment within the workshop. Facilitators and participants worked together to co-create, maintain, and protect a safe space in which to engage in therapy. Findings are discussed in relation to key aspects of facilitation and the participants’ response to the workshops. Recommendations for future workshop development are presented.

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)0.002
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 score0.092
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.103
GPT teacher head0.500
Teacher spread0.397 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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