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The influence of organisational context and practitioner attitudes on implementation of the carer support needs assessment tool (CSNAT) intervention

2016· article· en· W2517556538 on OpenAlexaboutno aff
Janet Diffin, Gail Ewing, Gunn Grande

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Psychological interventionPalliative careIntervention (counseling)Early adopterPsychologyNursingService (business)MedicineBusinessMarketing

Abstract

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Background The Carer Support Needs Assessment Tool (CSNAT) intervention identifies and addresses family carer support needs towards end of life.1–3A paucity of studies has investigated how to successfully implement evidence based interventions within palliative care. Aim Investigate how staff attitudes and organisational context affect implementation of the CSNAT intervention in palliative care. Methods 36 UK palliative care services participated. Staff surveys measured attitudes and context prior to, and six months after the implementation began including (i) a questionnaire assessing staff attitudes to the CSNAT; (ii) The Alberta Context Tool (ACT) assessing organisational context. Data on use of the CSNAT intervention were collected over six months; services were classified as ‘high’ or ‘low’ adopters on this basis. Relationships between service characteristics, aggregate data on staff attitudes and organisational context, and level of adoption were analysed. Results 157/462 surveys were returned at baseline and 69/462 at six months. Level of adoption depended on service type. ‘High’ adopters had a higher ratio of intervention ‘champions’ to total staff numbers and higher scores for ACT ‘informal interactions’ (e.g. more discussions with colleagues about patient care), compared to ‘low’ adopters. Both groups had similarly positive attitudes to the CSNAT intervention pre-implementation. By six months attitudes for ‘low’ adopters were significantly more negative, but remained similar or improved for ‘high’ adopters. Conclusions Ensuring successful implementation of complex interventions within palliative care requires consideration of the organisational context, service type, strategies for maintaining positive staff attitudes over time, and the use of intervention ‘champions’. References Ewing G, Brundle C, Payne S, Grande G. The Carer Support Needs Assessment Tool (CSNAT) for use in palliative and end-of-life care at home: A validation study.J Pain Symptom Manage2013;46(3):395–405 Ewing G, Grande G. Development of a Carer Support Needs Assessment Tool (CSNAT) for end-of-life care practice at home: a qualitative study.Palliat Med2013;27(3):244–256 Ewing G, Austin L, Diffin J, Grande G. The Carer Support Needs Assessment Tool: A person centred approach to carer assessment and support.Br J Community Nurs2015;20(12):580–584

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.465
Teacher spread0.400 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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