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The Effectiveness of Acceptance and Commitment Therapy (ACT) in Reducing Pain Intensity and Enhancing the Sense of Coherence and Psychological Well-being among the Patients with Chronic Low Back Pain

2020· article· en· W3082682598 on OpenAlexaboutno aff
Hossein Jenaabadi, S. Maryam Hosseini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsAcceptance and commitment therapyChronic painPhysical therapyMedicinePopulationDescriptive statisticsMcGill Pain QuestionnairePsychological interventionPsychologyClinical psychologyPsychiatryIntervention (counseling)Visual analogue scale

Abstract

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Chronic pain is a common, multifactorial problem that requires medical and psychological interventions to be managed. On the other hand, Acceptance and commitment therapy (ACT) is one of the third-wave cognitive-behavioral therapies, which has recently been used to treat the certain psychiatric disorders and to enhance the patients’ psychological status. Therefore, the present study aimed to investigate the effectiveness of acceptance and commitment therapy on reducing pain intensity and improving the sense of coherence and psychological well-being among the patients with chronic low back pain. This quasi-experimental study was performed by a pretest-posttest design with two groups. Also, it had a statistical population including all the patients with chronic low back pain and were present at the neuropsychiatry clinic of Ali ibn Abi Talib Hospital, who were referred to Red Crescent Physiotherapy Clinic in Zahedan from March 2016 to May 2017. Moreover, its sample consisted of 30 patients with chronic low back pain who were selected from all the patients referred to Red Crescent Physiotherapy Clinic in Zahedan, using the targeted sampling method in terms of the inclusion and exclusion criteria. These patients were then assigned into two groups as experimental and control, each one included 15 patients. In addition, the McGill Pain Questionnaire (1997), the Ryff Psychological Well-being Scale (1989), and the Sense of Coherence Scale designed by Flensborg-Madson et al. (2006) were used as data collection tools. To analyze the data, descriptive statistics such as means, standard deviations, and univariate and multivariate covariance analyses were used. The data analysis indicated that, the acceptance and commitment therapy could significantly decrease the pain intensity and its subscales among the patients in the experimental group compared to the control group (p < 0.01). Furthermore, acceptance and commitment therapy significantly increased the sense of coherence and psychological well-being as well as their subscales in the experimental group's patients compared to the control group (p < 0.01). It can be concluded that, acceptance and commitment therapy was effective on reducing the pain intensity and boosting the sense of coherence and psychological well-being among the patients with chronic low back pain. Therefore, the findings represent new horizons in clinical interventions and can be used as an effective intervention method.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.345
Teacher spread0.318 · 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 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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