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Table 2 A&F propositions, data sources used to code propositions, and pattern matching results

From: Using audit and feedback to guide tailored implementations of measurement-based care in community mental health: a multiple case study

Proposition

Data source

Pattern matching results

No

Description

Audit report

Strategy tracking

Auditor log

Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

1

MBC penetration will increase when implementation team members believe that audit reports accurately represent their clinical performance

 

X

X

50%

33%

50%

17%

43%

33%

2

MBC penetration will increase when audit reports include information that is relevant to MBC barriers identified by implementation team members during team meetings

X

X

 

83% b

33%

83%b

17%

43%

33%

3

MBC penetration will increase when the audit report includes data that is reported at the individual clinician level

X

  

83% b

67%a

83%b

67%a

29%

83%b

4

MBC penetration will increase when implementation team members make intentional adjustments to the design, format, or contents of the audit reports

X

X

X

50%

50%

50%

67%a

29%

33%

5

MBC penetration will increase when requests to adjust the design, format, or content audit reports are successfully accommodated by researchers or other clinic staff

X

X

X

67%a

17%

67%a

83%b

43%

33%

  1. Audit report = data for confirming proposition was collected from audit report records delivered to implementation teams
  2. Strategy tracking = data for confirming proposition was collected from the strategy tracking document
  3. Auditor log = data for confirming proposition was collected from the auditor request log
  4. Pattern matching results = percentage of cases in which satisfied propositions were followed by increased MBC penetration scores
  5. aSatisfied propositions were followed by increased penetration in over 50% of implementation team meetings
  6. bSatisfied propositions were followed by increased penetration in over 80% of implementation team meetings