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Launching the Tidal Model: evaluating the evidence.

W Gordon1, T Morton, G Brooks

  • 1Birmingham and Solihull NHS Mental Health Trust, Birmingham, UK. bill_tidal_model@runbox.com

Journal of Psychiatric and Mental Health Nursing
|December 13, 2005
PubMed
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The Tidal Model significantly reduced serious incidents by 57% and improved nurse and patient satisfaction in mental health wards. Evidence supports its wider implementation in NHS Trusts.

Area of Science:

  • Mental Health Nursing
  • Healthcare Quality Improvement

Background:

  • The Tidal Model is an innovative approach to care within acute mental health settings.
  • Previous evaluations have yielded positive results but faced criticism regarding correlational evidence.

Purpose of the Study:

  • To evaluate the effectiveness of the Tidal Model in two acute admission wards.
  • To examine the criteria and reasoning for evaluating evidence generated by the model's implementation.
  • To compare findings from Birmingham (2004) and Newcastle (2001) studies.

Main Methods:

  • Comparative analysis of data from two distinct evaluations of the Tidal Model.
  • Qualitative and quantitative assessment of patient and nurse outcomes.
  • Philosophical examination of evidence and proof using Aristotelian and Peircean frameworks.

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Main Results:

  • Birmingham study: 57% decrease in serious untoward incidents (assault, violence, harassment) post-Tidal Model implementation.
  • Significant improvements in nurse job satisfaction and positive inpatient service user assessments of care quality.
  • Comparison with Newcastle study (2001) reinforces positive outcomes, addressing criticisms of earlier correlational evidence.

Conclusions:

  • The evidence from both studies provides a reasonable inference to the best explanation for the Tidal Model's positive impact.
  • The results are probable and sufficient to recommend wider implementation of the Tidal Model across Mental Health NHS Trusts.
  • Philosophical considerations of evidence support the validity of inferring causality from observed outcomes.