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Applying ‘best fit’ frameworks to systematic review data extraction Andrea Miller-Nesbitt, Catherine Boden, Andrew Booth, et al. 7 th International Conference on Qualitative and Quantitative Methods in Libraries, Paris France May 2015

Applying ‘best fit’ frameworks to systematic review data extraction

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Page 1: Applying ‘best fit’ frameworks to systematic review data extraction

Applying ‘best fit’ frameworks to systematic review data extraction Andrea Mil ler-Nesbitt, Catherine Boden, Andrew Booth, et al. 7 t h I n te rna t i ona l Con fe rence on Qua l i t a t i ve and Quan t i t a t i ve Me thods i n L ib ra r i es , Pa r i s F rance

May 2015

Page 2: Applying ‘best fit’ frameworks to systematic review data extraction

Overview • Background  • Systema2c  review  process  • ‘Best  fit’  framework  methodology  • Applica2on  of  methodology  to  our  project  

Page 3: Applying ‘best fit’ frameworks to systematic review data extraction

Background

Conduct  a  systema2c  review  to  address  one  of  the  15  ques2ons  iden2fied  in  the  MLA  Research  Agenda:  Appraising  the  Best  Available  Evidence  

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Research question

What  skills  and  knowledge  must  librarians  possess  in  order  to  be  able  to  design  tools    to  help  researchers  visualize,  mine,  and    otherwise  manage  large  and  complex  data  gathered  during  both  quan2ta2ve  

and  qualita2ve  research?  

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Research question

What  skills  and  knowledge  must  librarians  possess  in  order  to  be  able  to  design  tools    to  help  researchers  visualize,  mine,  and    otherwise  manage  large  and  complex  data  gathered  during  both  quan2ta2ve  

and  qualita2ve  research?  

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*Catherine    Boden    

Brooke  Billman  

Lorely  Ambriz  

Andrea  Miller-­‐Nesbitt  Martin  Morris  

Andrew    Booth  

Abby  Adamczyk  

Anne  Woznica  

Keith  Engwall  

Rienne    Johnson  

Betsy  Clark  

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Search Databases:  PubMed,  Embase,  ACM,  LISA,  LISTA,  ERIC,  Web  of  Science,  WorldCat  

Date  limits:  Ar2cles  

•  2000  to  May  2014  Books  

•  2005  to  May  2014  

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Preliminary results

Records  a6er  duplicates  removed  (n  =  3910)

Records  screened  (n  =    3910  )  

Records  excluded  (n  =    3745  )

Full-­‐text  arEcles  assessed  for  eligibility  (n  =  165)  

101  reviewed 64  in  progress

Studies  included  in  qualitaEve  synthesis  (by  April  24  2015)  

(n  =  26  )

9  –  data  mining 7  –  data  visualizaEon  24  –  data  management

 

Note:  arEcles  could  be  coded  in  

more  than  one  category

Full-­‐text  arEcles  excluded  to  date  (n  =  70)    

5  –  not  English 13  –  not  about  research  data  

management,  mining  or  visualizaEon   32  –  not  about  designing  tools   12  –  did  not  address  librarian  

competencies 3  –  librarian  competencies  were  

described  but  not  in  relaEon  to  research  data

3-­‐  insufficiently  complete  for  data  extracEon

Records  aTer  duplicates  removed  (n=3910)  

Records  screened  (n=3910)  

Full-­‐text  ar2cles  assessed  for  eligibility  (n=165)  

Ar2cles  included  in  qualita2ve  analysis  

(n=26*)  

Data  visualiza2on  (n=7)  Data  mining  (n=9)  

Data  management  (n=24)  

Records  excluded  in  2tle  abstract  screen  (n=3745)  

Full-­‐text  ar2cles  excluded  (n=70*)  

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Data extraction

“Best  fit”  framework  synthesis  

Large  result  set  

Time  constraints  

Large  research  team  

MulEple  facets  within  research  quesEon  

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Framework synthesis • Deduc2ve  process  used  for  systema2c  reviews  • Highly  structured  approach  to  analyzing  qualita2ve  data  • A  priori  framework  is  iden2fied  or  developed  from  a  range  of  sources  

• Clearly  defined  themes  in  order  to  ensure  transparency,  consistency  and  speed  of  data  coding  

 

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‘Best fit’ framework synthesis

“The  ‘best  fit’  framework  synthesis  method  offered  a  means  to  test,  reinforce  and  build  on  an  exis2ng  published  model,  

conceived  for  a  poten2ally  different  but  relevant  popula2on…this  approach  

produces  a  rela2vely  rapid,  transparent  and  pragma2c  process.”  

(Carroll  et  al.,  2013,  p1)  

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Research  ques2on  

Iden2fy  ‘best  fit’  frameworks,  conceptual  models  or  theories  

Iden2fy  relevant  studies  for  analysis    

Generate  a  priori  framework  using  thema2c  analysis  

Extract  data  from  included  studies  

Code  evidence  from  included  studies  against  a  priori  framework  

Create  new  themes  by  doing  thema2c  analysis  on  evidence  that  cannot  be  coded  against  the  framework  

Incorporate  new  themes  into  a  priori  framework  to  produce  new  conceptual  model  

(Carroll  et  al.,  2013,  p.3)  

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‘Best fit’ framework(s) synthesis

• DigCCurr  Data  management  

• TBD  Data  mining  

Data  visualiza2on   • TBD  

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Data extraction form

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Data extraction form “what  are  the  competencies  described  in  the  ar2cle  for  designing  tools  that  support  archival  storage?”  

“what  are  the  competencies  in  the  ar2cle  for  designing  tools  for  valida2on  and  quality  control  of  digital  objects/packages?”  

…  

“descrip2on  of  competencies  relevant  to  the  design  of  tools  for  data  management  that  did  not  fit  in  the  categories  above”  

“there  is  insufficient  data  to  be  extracted  with  regard  to  research  data  management”  

“record  any  obvious  issues  about  study  quality”  

 

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Challenges

• Iden2fying  appropriate  frameworks  • Lack  of  granularity  • Missing  various  concepts  (especially  tools)  • Did  not  adequately  address  ‘competencies’  

• Maintaining  objec2vity  • Ensuring  we  do  not  force  data  into  the  a  priori  framework    

 

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Next steps

• New  themes  iden2fied  • Relevance  of  data  management,  mining  or  visualiza2on  frameworks  to  librarians’  roles  • Applica2on  of  ‘best  fit’  framework  methodology  to  LIS  research  

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Projected outcome

Generate  an  evidence-­‐based  model,  that  iden2fies  the  competencies  required  of  librarians  involved  in  the  design  of  tools  used  for  data  management,  mining  or  visualiza2on.    

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Selected references Barnek-­‐Page,  E.,  &  Thomas,  J.  (2009).  Methods  for  the  synthesis  of  qualita2ve  research:  a  cri2cal  review.  BMC  Medical  Research  Methodology,  9(1),  1-­‐11.    

Carroll,  C.,  Booth,  A.,  &  Cooper,  K.  (2011).  A  worked  example  of  "best  fit"  framework  synthesis:  a  systema2c  review  of  views  concerning  the  taking  of  some  poten2al  chemopreven2ve  agents.  BMC  medical  research  methodology,  11(29).    

Carroll,  C.,  Booth,  A.,  Leaviss,  J.,  &  Rick,  J.  (2013).  "Best  fit"  framework  synthesis:  refining  the  method.  BMC  medical  research  methodology,  13(1),  37.    

Dixon-­‐Woods,  M.  (2011).  Using  framework-­‐based  synthesis  for  conduc2ng  reviews  of  qualita2ve  studies.  BMC  medicine,  9(1),  39.    

Eldredge,  J.  D.,  Ascher,  M.  T.,  Holmes,  H.  N.,  &  Harris,  M.  R.  (2012).  The  new  Medical  Library  Associa2on  research  agenda:  final  results  from  a  three-­‐phase  Delphi  study.  J  Med  Libr  Assoc,  100(3),  214-­‐218.  doi:  10.3163/1536-­‐5050.100.3.012  

Eldredge,  J.  D.,  Harris,  M.  R.,  &  Ascher,  M.  T.  (2009).  Defining  the  Medical  Library  Associa2on  research  agenda:  methodology  and  final  results  from  a  consensus  process.  J  Med  Libr  Assoc,  97(3),  178-­‐185.  

Ritchie,  J.,  &  Spencer,  L.  (1994).  Qualita2ve  data  analysis  for  applied  policy  research.  In  A.  Bryman  &  R.  G.  Burgess  (Eds.),  Analyzing  qualitaNve  data  (pp.  173-­‐194).  London;  New  York:  Routledge.  

 

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Questions? Andrea  Miller-­‐Nesbi\  McGill  University,  Montreal,  QC  andrea.miller-­‐[email protected]