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Record W2965642521 · doi:10.1089/act.2019.29231.jas

Horticultural Therapy as an Intervention for Schizophrenia: A Review

2019· review· en· W2965642521 on OpenAlexaboutno aff
Jaime Ascencio

Bibliographic record

VenueAlternative and Complementary Therapies · 2019
Typereview
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Intervention (counseling)Set (abstract data type)Mental illnessPsychologyScale (ratio)NeuropsychologyVocational educationQuality (philosophy)Psychological interventionPsychiatryClinical psychologyMental healthPsychotherapistMedicineCognitionComputer science

Abstract

fetched live from OpenAlex

Background: Schizophrenia is a mental illness that impacts multiple domains of a person's functioning therefore requiring multiple methods of treatment, which can become time consuming and costly. Horticultural therapy (HT) is a plant-based approach to reaching client goals and can do so in a holistic manner. Methods: A literature review was conducted in Google Scholar for “horticultural therapy” + “schizophrenia” and resulted in five relevant articles. These articles were evaluated through the Newcastle–Ottawa Scale and assigned a quality score. Results: HT administration and measurement were done differently across studies, yet HT was found to improve functioning in multiple domains, including social, vocational, psychological, and neuropsychological. Conclusions: HT seems to be an effective approach to addressing client concerns in more than one domain. This suggests that HT may be a more affordable, integrative, and time-conscious intervention for people with schizophrenia. However, the studies reviewed were of low quality and make it unwise to draw definitive conclusions. Future studies should follow set standards for conducting and reporting research.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.177
GPT teacher head0.456
Teacher spread0.279 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations9
Published2019
Admission routes1
Has abstractyes

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